Analyze.rmd
Description:
  This script takes inputs in the form of counts matrices and dataframes
  formatted for analysis by the CountstoAnalysis scripts for the 001 and
  002 datasets (with all calculations and formatting already completed) –
  See Inputs for all inputs. The script then conducts all summary
  calculations, statistical tests, and produces all visuals and output tables
  found in the main text and supplement (unless otherwise noted below – see
  Outputs). Code blocks appear in more-or-less the same order that data are
  presented in the text; brief descriptions head each box and more detailed
  code can be found in-line. This script outputs a set of figures and tables
  that are included, some with modification and stylization, in the main text
  and supplement.
Inputs:
- poc1.Rdata – CountstoAnalysis formatted dataframe for POC fitness assay single-barcode controls.
- poc2.Rdata – CountstoAnalysis formatted dataframe for POC fitness assay dual-barcode controls.
- poc92.Rdata – CountstoAnalysis formatted dataframe for POC fitness assay fitness assay samples.
- 001_counts.expected.Rdata – FastqtoCounts formatted matrix for POC fitness assay library expected (by design) counts.
- 001_counts.unexpected.Rdata – FastqtoCounts formatted matrix for POC fitness assay library unexpected (barcode cross-contamination) counts.
- 002_CC_counts.expected.Rdata – FastqtoCounts formatted matrix 250-generation evolution experiment expected (by design) counts – consensus matrix constructed from two sequenced libraries.
- 002_CC_counts.unexpected.Rdata – FastqtoCounts formatted matrix 250-generation evolution experiment unexpected (barcode cross-contamination) counts – consensus matrix from two sequenced libraries.
- 002_counts.expected.Rdata – FastqtoCounts formatted matrix 250-generation evolution experiment expected (by design) counts – original sequenced library.
- 002_counts.unexpected.Rdata – FastqtoCounts formatted matrix 250-generation evolution experiment unexpected (barcode cross-contamination) counts – original sequenced library.
- 002_Reruns_counts.expected.Rdata – FastqtoCounts formatted matrix 250-generation evolution experiment expected (by design) counts – resequenced samples library.
- 002_Reruns_counts.unexpected.Rdata – FastqtoCounts formatted matrix 250-generation evolution experiment unexpected (barcode cross-contamination) counts – resequenced samples library.
- myfa.Rdata – CountstoAnalysis formatted dataframe for 250-generation experimental evolution project fitness assays.
- myevo.Rdata – CountstoAnalysis formatted dataframe for 250-generation experimental evolution project longitudinal samples.
Outputs:
Main Figures: 1. 000_Figure_2.pdf 2. 000_Figure_3.pdf 3. 000_Figure_4.pdf 4. 000_Figure_5.pdf
Additional Files: 1. 000_Additional_File_4.html 2. 000_Additional_File_5.html 3. 000_Additional_File_6.html 4. 000_Additional_File_7.pdf 5. 000_Additional_File_8.pdf 6. 000_Additional_File_9.html 7. 000_Additional_File_10.html 8. 000_Additional_File_11.html 9. 000_Additional_File_12.html 10. 000_Additional_File_13.html 11. 000_Additional_File_14.html 12. 000_Additional_File_15.pdf 13. 000_Additional_File_16.html 14. 000_Additional_File_17.pdf 15. 000_Additional_File_18.html 16. 000_Additional_File_19.pdf 17. 000_Additional_File_20.html 18. 000_Additional_File_21.pdf 19. 000_Additional_File_22.html 20. 000_Additional_File_23.pdf 21. 000_Additional_File_24.html 22. 000_Additional_File_25.pdf 23. 000_Additional_File_26.html 24. 000_Additional_File_27.pdf
Figure_1, is not output by this script; it is manually created in excel
Table_1 is not created directly from this script, but is built from outputs associated with Additional Files 14:27.
Additional Files 1, 2, and 3 are created by hand in excel and are not created by this script
Additional_File_4 is output with raw p-values, but values are converted to fdr for publication and presented in an xlsx format document. Additional_File_12 is output with raw 2-sided test p-values, but values are converted to 1-sided (higher) and fdr for publication and presented in an xlsx format document.
Begin Code Blocks:
Prepare the workspace: clean, set options, load packages, load and final formatting on inputs.
rm(list = ls()) # clear workspace
knitr::opts_chunk$set(tidy = TRUE, collapse = TRUE) # set global knitr options.
# load required packages
require(ggplot2); # used for plotting #check#
require(pwr); # used for power analysis
require(lme4); # used for mixed effects models #check#
require(lmerTest); # used to interpret mixed effects models #check#
require(sjPlot); # used to output tables #check#
require(weights); # used for weighted t.test #check#
require(plyr); # used for dataframe manipulation #check#
require(gridExtra) # used for multipanel plotting #check#
require(MuMIn) # model seleciton and multimodel inference
options(scipen = 999) # turn off scientific notationLoad the required datasets and Conduct any last minute calculations
dir_in_pocpro <- "2_CountstoAnalysis/001_Prepare_Datasets" # input directory for Proof of concept processed dataframes
setwd("..")
setwd(dir_in_pocpro) # set to above directory path
load(file = "poc1.Rdata") # load the 1bc control processed dataframe
load(file = "poc2.Rdata") # load the 2 bc control processed dataframe
load(file = "poc92.Rdata") # load the 92 bc proof of concept fitness assays processed dataframe
dir_in_pocraw <- "001_Raw_Data_Metadata" # input directory for Proof of concept raw dataframes
setwd("..")
setwd(dir_in_pocraw) # set to above directory path
load(file = "001_counts.expected.Rdata")
poc.ce <- (counts.expected + 1)[c(1:92), ]
rm(counts.expected) # expected counts
load(file = "001_counts.unexpected.Rdata")
poc.cc <- (counts.unexpected + 1)[c(1:92), ]
rm(counts.unexpected) # cross contamination counts.
dir_in_faevoraw <- "002_Raw_Data_Metadata" # input directory for FA and Evo raw dataframes
setwd("..")
setwd(dir_in_faevoraw) # set to above directory path
load(file = "002_CC_counts.expected.Rdata")
faevo.ce <- (ects + 0)
rm(ects) # expected counts {combined}
load(file = "002_CC_counts.unexpected.Rdata")
faevo.cc <- (ccts + 0)
rm(ccts) # cross contamination counts.{combined}
load(file = "002_counts.expected.Rdata")
faevo.1.ce <- (counts.expected + 1)[c(1:78), ]
rm(counts.expected) # expected counts {first run samples}
load(file = "002_counts.unexpected.Rdata")
faevo.1.cc <- (counts.unexpected + 1)[c(1:78), ]
rm(counts.unexpected) # cross contamination counts. {first run samples}
load(file = "002_Reruns_counts.expected.Rdata")
faevo.2.ce <- (counts.expected + 1)[c(1:78), ]
rm(counts.expected) # expected counts {rerun samples}
load(file = "002_Reruns_counts.unexpected.Rdata")
faevo.2.cc <- (counts.unexpected + 1)[c(1:78), ]
rm(counts.unexpected) # cross contamination counts. {rerun samples}
dir_in_faevopro <- "002_Prepare_Datasets" # input directory for FA and Evo processed dataframes
setwd("..")
setwd(dir_in_faevopro) # set to above directory path
load(file = "myfa.Rdata") # fitness assay
load(file = "myevo.Rdata") # evolution
rm(dir_in_faevopro, dir_in_faevoraw, dir_in_pocpro, dir_in_pocraw)Create helper functions for linear model residual plotting and data transformation.
# expand_residuals creates a modified histogram of residuals. Each entry in
# the model output is plotted a number of times equal to its reads.
expand_residuals <- function(model, reads) {
myfr <- NA
for (i in 1:length(residuals(model))) {
times <- (((reads[i] - min(reads, na.rm = T))/(max(reads) - min(reads))) *
1000) + 1
myfr <- c(myfr, rep(residuals(model)[i], times = times))
}
return(myfr)
}
# log transformation for modeling.
log_transform <- function(myvar) {
myvar <- log(((myvar - min(myvar))/(max(myvar) - min(myvar))) + 1)
return(myvar)
}
# get legend function found on stack exchange
# ---------------------------------- Source:
# https://stackoverflow.com/questions/12041042/how-to-plot-just-the-legends-in-ggplot2
# scrapes figure legend for later plotting in multipannel plot
get_legend <- function(myggplot) {
tmp <- ggplot_gtable(ggplot_build(myggplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)
}Set color palette options for all plots
setpalette <- RColorBrewer::brewer.pal(6, "Dark2") # current preferred palette
# setpalette <-
# c('#993404','#d95f0e','#fe9929','#41b6c4','#2c7fb8','#253494') # option 2
# setpalette <- c('#8c510a','#d8b365','#f6e8c3',
# '#c7eae5','#5ab4ac','#01665e') # option 3Analysis: Assess POC fitness assay barcode fitness equivalence.
my <- poc92[order(poc92$bcid), ]
# model & diagnostic plots
# ----------------------------------------------------
my$rw_corrected <- my$rw - weighted.mean(my$rw, my$r)
mymod <- lm(my$rw_corrected ~ my$bcid + 0, weights = my$r)
summary(mymod)
##
## Call:
## lm(formula = my$rw_corrected ~ my$bcid + 0, weights = my$r)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -4.7350 -0.5858 -0.0109 0.5304 5.3405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## my$bcidd1A10 0.00132555 0.00371437 0.357 0.721280
## my$bcidd1A11 -0.00762370 0.00466055 -1.636 0.102267
## my$bcidd1A2 0.00811450 0.00396019 2.049 0.040778 *
## my$bcidd1A3 0.00494242 0.00439553 1.124 0.261165
## my$bcidd1A4 0.00795573 0.00448555 1.774 0.076495 .
## my$bcidd1A5 0.00103320 0.00520070 0.199 0.842573
## my$bcidd1A6 -0.00085773 0.00357809 -0.240 0.810610
## my$bcidd1A7 0.00344369 0.00512097 0.672 0.501476
## my$bcidd1A8 0.00583557 0.00414724 1.407 0.159778
## my$bcidd1A9 0.00724382 0.00374750 1.933 0.053584 .
## my$bcidd1B1 -0.00527477 0.00502129 -1.050 0.293807
## my$bcidd1B10 -0.00821202 0.00615734 -1.334 0.182674
## my$bcidd1B11 -0.01330702 0.00511428 -2.602 0.009437 **
## my$bcidd1B12 -0.00741678 0.00553232 -1.341 0.180413
## my$bcidd1B2 0.00289719 0.00431315 0.672 0.501957
## my$bcidd1B3 0.00354456 0.00434513 0.816 0.414878
## my$bcidd1B4 0.00951091 0.00421461 2.257 0.024293 *
## my$bcidd1B5 0.00961576 0.00421606 2.281 0.022819 *
## my$bcidd1B6 0.00134667 0.00501745 0.268 0.788462
## my$bcidd1B7 -0.00398474 0.00435334 -0.915 0.360287
## my$bcidd1B8 0.00386754 0.00435584 0.888 0.374856
## my$bcidd1B9 -0.00197406 0.00517408 -0.382 0.702909
## my$bcidd1C1 -0.00130215 0.00523917 -0.249 0.803778
## my$bcidd1C10 -0.00200878 0.00612990 -0.328 0.743221
## my$bcidd1C11 0.00332553 0.00513862 0.647 0.517707
## my$bcidd1C12 -0.00495638 0.00525931 -0.942 0.346266
## my$bcidd1C2 0.00310014 0.00440966 0.703 0.482235
## my$bcidd1C3 0.00170870 0.00544151 0.314 0.753591
## my$bcidd1C4 0.00064326 0.00445091 0.145 0.885124
## my$bcidd1C5 -0.00293377 0.00470909 -0.623 0.533457
## my$bcidd1C6 -0.00108375 0.00401081 -0.270 0.787069
## my$bcidd1C7 -0.00224296 0.00857907 -0.261 0.793814
## my$bcidd1C8 -0.00815331 0.00535937 -1.521 0.128566
## my$bcidd1C9 0.01983075 0.00598788 3.312 0.000968 ***
## my$bcidd1D1 0.00371456 0.00415556 0.894 0.371651
## my$bcidd1D10 0.01036458 0.00405675 2.555 0.010802 *
## my$bcidd1D11 -0.00004981 0.00380942 -0.013 0.989570
## my$bcidd1D12 0.00301900 0.00386910 0.780 0.435449
## my$bcidd1D2 -0.01098378 0.02236414 -0.491 0.623464
## my$bcidd1D3 -0.00440055 0.00562205 -0.783 0.434011
## my$bcidd1D4 -0.00693858 0.00737022 -0.941 0.346760
## my$bcidd1D5 -0.00326940 0.00563709 -0.580 0.562087
## my$bcidd1D6 -0.00844038 0.00554061 -1.523 0.128053
## my$bcidd1D7 -0.00168313 0.00462568 -0.364 0.716052
## my$bcidd1D8 0.00180750 0.00397535 0.455 0.649462
## my$bcidd1D9 0.00014080 0.00545635 0.026 0.979419
## my$bcidd1E1 -0.01270595 0.00523534 -2.427 0.015441 *
## my$bcidd1E10 -0.00147548 0.00433537 -0.340 0.733691
## my$bcidd1E11 -0.00708010 0.00406068 -1.744 0.081609 .
## my$bcidd1E12 0.01157108 0.00442433 2.615 0.009078 **
## my$bcidd1E2 0.00500682 0.00373187 1.342 0.180085
## my$bcidd1E3 0.00762603 0.00554298 1.376 0.169259
## my$bcidd1E4 0.00591987 0.00384425 1.540 0.123964
## my$bcidd1E5 -0.00657763 0.00498803 -1.319 0.187644
## my$bcidd1E6 -0.00139332 0.00372470 -0.374 0.708444
## my$bcidd1E7 0.00852322 0.00396829 2.148 0.032020 *
## my$bcidd1E8 -0.00369015 0.00443221 -0.833 0.405327
## my$bcidd1E9 0.00639603 0.00436339 1.466 0.143076
## my$bcidd1F1 -0.00012498 0.00381935 -0.033 0.973904
## my$bcidd1F10 0.00393912 0.00423405 0.930 0.352467
## my$bcidd1F11 0.00670256 0.00469224 1.428 0.153547
## my$bcidd1F12 0.00778245 0.00385357 2.020 0.043756 *
## my$bcidd1F2 0.00582870 0.00435141 1.339 0.180780
## my$bcidd1F3 0.00651265 0.00596554 1.092 0.275281
## my$bcidd1F4 -0.02625366 0.00923796 -2.842 0.004596 **
## my$bcidd1F5 -0.04834088 0.00429859 -11.246 < 0.0000000000000002 ***
## my$bcidd1F6 -0.00012930 0.00381509 -0.034 0.972972
## my$bcidd1F7 -0.01483489 0.00492987 -3.009 0.002700 **
## my$bcidd1F8 -0.00220117 0.00605837 -0.363 0.716455
## my$bcidd1F9 0.00029767 0.01166664 0.026 0.979651
## my$bcidd1G1 0.01006985 0.00654635 1.538 0.124376
## my$bcidd1G10 0.00690433 0.00358418 1.926 0.054408 .
## my$bcidd1G11 0.00299309 0.00557059 0.537 0.591205
## my$bcidd1G12 0.00147423 0.00344561 0.428 0.668867
## my$bcidd1G2 0.00615615 0.00569582 1.081 0.280096
## my$bcidd1G3 0.00097634 0.00390830 0.250 0.802795
## my$bcidd1G4 -0.00335673 0.00478773 -0.701 0.483433
## my$bcidd1G5 -0.01510445 0.00537164 -2.812 0.005043 **
## my$bcidd1G6 -0.00650477 0.00473025 -1.375 0.169463
## my$bcidd1G7 -0.00911754 0.00430618 -2.117 0.034534 *
## my$bcidd1G8 -0.00467010 0.00489124 -0.955 0.339967
## my$bcidd1G9 -0.00345997 0.00470652 -0.735 0.462463
## my$bcidd1H11 0.00188567 0.00387909 0.486 0.627018
## my$bcidd1H2 -0.00688077 0.00535513 -1.285 0.199193
## my$bcidd1H3 -0.00074760 0.00640698 -0.117 0.907138
## my$bcidd1H4 -0.03016766 0.00925002 -3.261 0.001155 **
## my$bcidd1H5 -0.00289320 0.00438300 -0.660 0.509377
## my$bcidd1H6 0.00117090 0.00365259 0.321 0.748621
## my$bcidd1H7 -0.00459377 0.00852914 -0.539 0.590312
## my$bcidd1H8 -0.00449698 0.00614172 -0.732 0.464255
## my$bcidd1H9 -0.00798745 0.00584794 -1.366 0.172359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.127 on 819 degrees of freedom
## Multiple R-squared: 0.2653, Adjusted R-squared: 0.1836
## F-statistic: 3.25 on 91 and 819 DF, p-value: < 0.00000000000000022
hist(residuals(mymod), breaks = 50) # residuals look good, check the other diagnostic plots too...
hist(expand_residuals(mymod, my$r), breaks = 50) # this more accurately represents the distribution of residuals in the model output.
plot(mymod) # view the diagnostic plots
paste0("Number of BCs with fitness different than pop mean = ", sum(p.adjust(coef(summary(mymod))[,
4], method = "fdr") < 0.05)) # number of barcodes with fit different than pop weighted mean.
## [1] "Number of BCs with fitness different than pop mean = 3"
paste0("RsMSE = ", sqrt(mean(residuals(mymod)^2))) # report rmse (requires unscalled lm model)
## [1] "RsMSE = 0.0175768866199638"
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_4.html")| Â | my$rw corrected | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| d 1 A 10 | 0.00133 | -0.00595 – 0.00861 | 0.35687 | 0.72127977 |
| d 1 A 11 | -0.00762 | -0.01676 – 0.00151 | -1.63579 | 0.10226678 |
| d 1 A 2 | 0.00811 | 0.00035 – 0.01588 | 2.04902 | 0.04077841 |
| d 1 A 3 | 0.00494 | -0.00367 – 0.01356 | 1.12442 | 0.26116464 |
| d 1 A 4 | 0.00796 | -0.00084 – 0.01675 | 1.77364 | 0.07649506 |
| d 1 A 5 | 0.00103 | -0.00916 – 0.01123 | 0.19867 | 0.84257323 |
| d 1 A 6 | -0.00086 | -0.00787 – 0.00616 | -0.23972 | 0.81061012 |
| d 1 A 7 | 0.00344 | -0.00659 – 0.01348 | 0.67247 | 0.50147576 |
| d 1 A 8 | 0.00584 | -0.00229 – 0.01396 | 1.40710 | 0.15977841 |
| d 1 A 9 | 0.00724 | -0.00010 – 0.01459 | 1.93297 | 0.05358384 |
| d 1 B 1 | -0.00527 | -0.01512 – 0.00457 | -1.05048 | 0.29380661 |
| d 1 B 10 | -0.00821 | -0.02028 – 0.00386 | -1.33370 | 0.18267430 |
| d 1 B 11 | -0.01331 | -0.02333 – -0.00328 | -2.60193 | 0.00943744 |
| d 1 B 12 | -0.00742 | -0.01826 – 0.00343 | -1.34063 | 0.18041306 |
| d 1 B 2 | 0.00290 | -0.00556 – 0.01135 | 0.67171 | 0.50195688 |
| d 1 B 3 | 0.00354 | -0.00497 – 0.01206 | 0.81575 | 0.41487762 |
| d 1 B 4 | 0.00951 | 0.00125 – 0.01777 | 2.25665 | 0.02429251 |
| d 1 B 5 | 0.00962 | 0.00135 – 0.01788 | 2.28075 | 0.02281947 |
| d 1 B 6 | 0.00135 | -0.00849 – 0.01118 | 0.26840 | 0.78846186 |
| d 1 B 7 | -0.00398 | -0.01252 – 0.00455 | -0.91533 | 0.36028749 |
| d 1 B 8 | 0.00387 | -0.00467 – 0.01240 | 0.88790 | 0.37485596 |
| d 1 B 9 | -0.00197 | -0.01212 – 0.00817 | -0.38153 | 0.70290904 |
| d 1 C 1 | -0.00130 | -0.01157 – 0.00897 | -0.24854 | 0.80377774 |
| d 1 C 10 | -0.00201 | -0.01402 – 0.01001 | -0.32770 | 0.74322104 |
| d 1 C 11 | 0.00333 | -0.00675 – 0.01340 | 0.64716 | 0.51770702 |
| d 1 C 12 | -0.00496 | -0.01526 – 0.00535 | -0.94240 | 0.34626553 |
| d 1 C 2 | 0.00310 | -0.00554 – 0.01174 | 0.70303 | 0.48223454 |
| d 1 C 3 | 0.00171 | -0.00896 – 0.01237 | 0.31401 | 0.75359137 |
| d 1 C 4 | 0.00064 | -0.00808 – 0.00937 | 0.14452 | 0.88512380 |
| d 1 C 5 | -0.00293 | -0.01216 – 0.00630 | -0.62300 | 0.53345671 |
| d 1 C 6 | -0.00108 | -0.00894 – 0.00678 | -0.27021 | 0.78706863 |
| d 1 C 7 | -0.00224 | -0.01906 – 0.01457 | -0.26145 | 0.79381434 |
| d 1 C 8 | -0.00815 | -0.01866 – 0.00235 | -1.52132 | 0.12856573 |
| d 1 C 9 | 0.01983 | 0.00809 – 0.03157 | 3.31181 | 0.00096754 |
| d 1 D 1 | 0.00371 | -0.00443 – 0.01186 | 0.89388 | 0.37165067 |
| d 1 D 10 | 0.01036 | 0.00241 – 0.01832 | 2.55490 | 0.01080165 |
| d 1 D 11 | -0.00005 | -0.00752 – 0.00742 | -0.01308 | 0.98957011 |
| d 1 D 12 | 0.00302 | -0.00456 – 0.01060 | 0.78029 | 0.43544853 |
| d 1 D 2 | -0.01098 | -0.05482 – 0.03285 | -0.49113 | 0.62346368 |
| d 1 D 3 | -0.00440 | -0.01542 – 0.00662 | -0.78273 | 0.43401108 |
| d 1 D 4 | -0.00694 | -0.02138 – 0.00751 | -0.94144 | 0.34675951 |
| d 1 D 5 | -0.00327 | -0.01432 – 0.00778 | -0.57998 | 0.56208732 |
| d 1 D 6 | -0.00844 | -0.01930 – 0.00242 | -1.52337 | 0.12805285 |
| d 1 D 7 | -0.00168 | -0.01075 – 0.00738 | -0.36387 | 0.71605183 |
| d 1 D 8 | 0.00181 | -0.00598 – 0.00960 | 0.45468 | 0.64946246 |
| d 1 D 9 | 0.00014 | -0.01055 – 0.01084 | 0.02581 | 0.97941897 |
| d 1 E 1 | -0.01271 | -0.02297 – -0.00244 | -2.42696 | 0.01544074 |
| d 1 E 10 | -0.00148 | -0.00997 – 0.00702 | -0.34034 | 0.73369072 |
| d 1 E 11 | -0.00708 | -0.01504 – 0.00088 | -1.74357 | 0.08160859 |
| d 1 E 12 | 0.01157 | 0.00290 – 0.02024 | 2.61533 | 0.00907808 |
| d 1 E 2 | 0.00501 | -0.00231 – 0.01232 | 1.34164 | 0.18008470 |
| d 1 E 3 | 0.00763 | -0.00324 – 0.01849 | 1.37580 | 0.16925925 |
| d 1 E 4 | 0.00592 | -0.00161 – 0.01345 | 1.53993 | 0.12396449 |
| d 1 E 5 | -0.00658 | -0.01635 – 0.00320 | -1.31868 | 0.18764413 |
| d 1 E 6 | -0.00139 | -0.00869 – 0.00591 | -0.37408 | 0.70844388 |
| d 1 E 7 | 0.00852 | 0.00075 – 0.01630 | 2.14783 | 0.03201970 |
| d 1 E 8 | -0.00369 | -0.01238 – 0.00500 | -0.83258 | 0.40532715 |
| d 1 E 9 | 0.00640 | -0.00216 – 0.01495 | 1.46584 | 0.14307587 |
| d 1 F 1 | -0.00012 | -0.00761 – 0.00736 | -0.03272 | 0.97390392 |
| d 1 F 10 | 0.00394 | -0.00436 – 0.01224 | 0.93034 | 0.35246715 |
| d 1 F 11 | 0.00670 | -0.00249 – 0.01590 | 1.42844 | 0.15354746 |
| d 1 F 12 | 0.00778 | 0.00023 – 0.01534 | 2.01954 | 0.04375588 |
| d 1 F 2 | 0.00583 | -0.00270 – 0.01436 | 1.33950 | 0.18078013 |
| d 1 F 3 | 0.00651 | -0.00518 – 0.01820 | 1.09171 | 0.27528055 |
| d 1 F 4 | -0.02625 | -0.04436 – -0.00815 | -2.84193 | 0.00459550 |
| d 1 F 5 | -0.04834 | -0.05677 – -0.03992 | -11.24575 | <0.001 |
| d 1 F 6 | -0.00013 | -0.00761 – 0.00735 | -0.03389 | 0.97297191 |
| d 1 F 7 | -0.01483 | -0.02450 – -0.00517 | -3.00919 | 0.00269975 |
| d 1 F 8 | -0.00220 | -0.01408 – 0.00967 | -0.36333 | 0.71645463 |
| d 1 F 9 | 0.00030 | -0.02257 – 0.02316 | 0.02551 | 0.97965060 |
| d 1 G 1 | 0.01007 | -0.00276 – 0.02290 | 1.53824 | 0.12437600 |
| d 1 G 10 | 0.00690 | -0.00012 – 0.01393 | 1.92634 | 0.05440800 |
| d 1 G 11 | 0.00299 | -0.00793 – 0.01391 | 0.53730 | 0.59120517 |
| d 1 G 12 | 0.00147 | -0.00528 – 0.00823 | 0.42786 | 0.66886664 |
| d 1 G 2 | 0.00616 | -0.00501 – 0.01732 | 1.08082 | 0.28009589 |
| d 1 G 3 | 0.00098 | -0.00668 – 0.00864 | 0.24981 | 0.80279516 |
| d 1 G 4 | -0.00336 | -0.01274 – 0.00603 | -0.70111 | 0.48343252 |
| d 1 G 5 | -0.01510 | -0.02563 – -0.00458 | -2.81189 | 0.00504284 |
| d 1 G 6 | -0.00650 | -0.01578 – 0.00277 | -1.37514 | 0.16946284 |
| d 1 G 7 | -0.00912 | -0.01756 – -0.00068 | -2.11732 | 0.03453391 |
| d 1 G 8 | -0.00467 | -0.01426 – 0.00492 | -0.95479 | 0.33996652 |
| d 1 G 9 | -0.00346 | -0.01268 – 0.00576 | -0.73514 | 0.46246280 |
| d 1 H 11 | 0.00189 | -0.00572 – 0.00949 | 0.48611 | 0.62701817 |
| d 1 H 2 | -0.00688 | -0.01738 – 0.00362 | -1.28489 | 0.19919285 |
| d 1 H 3 | -0.00075 | -0.01331 – 0.01181 | -0.11669 | 0.90713761 |
| d 1 H 4 | -0.03017 | -0.04830 – -0.01204 | -3.26136 | 0.00115463 |
| d 1 H 5 | -0.00289 | -0.01148 – 0.00570 | -0.66010 | 0.50937740 |
| d 1 H 6 | 0.00117 | -0.00599 – 0.00833 | 0.32057 | 0.74862140 |
| d 1 H 7 | -0.00459 | -0.02131 – 0.01212 | -0.53860 | 0.59031160 |
| d 1 H 8 | -0.00450 | -0.01653 – 0.00754 | -0.73220 | 0.46425473 |
| d 1 H 9 | -0.00799 | -0.01945 – 0.00347 | -1.36586 | 0.17235858 |
| Observations | 910 | |||
| R2 / R2 adjusted | 0.265 / 0.184 | |||
# -----------------------------------------------------------------------------
# create temporary plotting dataframe
# -----------------------------------------
myres <- as.data.frame(coef(summary(mymod)), stringsAsFactors = F) # create a myres dataframe to aid in plotting.
myres$p.adjust <- p.adjust(myres$`Pr(>|t|)`, method = "fdr") # adjusted p-values
myres$bcid <- gsub("^.*?d", "", rownames(myres)) # fix barcode id names
myres$mrw <- ddply(my, ~bcid, function(my) weighted.mean(my$rw, my$r))[, 2] # weighted mean for fitness
myres$rmrw <- ddply(my, ~bcid, function(my) mean(my$r))[, 2] # mean for weights
myres$mcmrw <- myres$mrw - weighted.mean(myres$mrw, myres$rmrw) # mean corrected fitness
myres$color <- "fdr > 0.05"
myres$color[myres$p.adjust <= 0.05] <- "fdr <= 0.05" # set plotting colors
myres$color <- as.factor(myres$color)
myres$color <- relevel(myres$color, "fdr > 0.05")
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- myres$mcmrw
myr <- myres$rmrw
myc <- myres$color
mybinwidth <- 0.0025
mytitle <- NULL
myylab <- "Count"
myxlab <- "Fitness difference from population mean"
myhighlight <- "black"
myfillapha <- 0.35
myptalpha <- 0.35
mylinealpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.345
mywidth <- 3.345
p <- ggplot(myres, aes(x = myx, color = myc, fill = myc)) + geom_histogram(alpha = myfillapha,
position = "identity", binwidth = mybinwidth) + geom_rug(alpha = myptalpha) +
# xlim(c(-0.06, 0.06)) +
ylim(c(NA, 15)) + geom_vline(xintercept = 0, linetype = "dashed", color = myhighlight) +
scale_color_manual(values = mypalette, guide = FALSE) + scale_fill_manual(values = mypalette) +
theme_classic() + theme(legend.position = c(0.8, 0.8)) + labs(fill = "Significance") +
ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) + geom_hline(yintercept = -0.01,
colour = "white", size = 1.01) + theme(axis.title.y = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = mytextsize,
angle = 0)) + theme(axis.text.x = element_text(size = mytextsize, angle = 0)) +
theme(legend.title = element_text(size = mytextsize, face = "bold")) + theme(legend.text = element_text(size = mytextsize)) +
scale_x_continuous(breaks = seq(-0.06, 0.06, by = 0.01), labels = c("-0.06",
"", "-0.04", "", "-0.02", "", "0", "", "0.02", "", "0.04", "", "0.06"),
limits = c(-0.06, 0.06))
p
## Warning: Removed 4 rows containing missing values (geom_bar).
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Figure_2.pdf", width = mywidth, height = myheight)
p
## Warning: Removed 4 rows containing missing values (geom_bar).
dev.off() # one column wide, equal height
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(p, myres, mymod, my, mybinwidth, myc, myfillapha, myheight, myhighlight,
mylinealpha, myptalpha, myr, mytextsize, mytitle, mywidth, myx, myxlab,
myylab, mypalette)Analysis: Calculate sequenced library reads summary values
paste0("matching counts (match f and r primer barcode, and moby bc): ", sum(c(poc.ce,
poc.cc, faevo.1.ce, faevo.1.cc, faevo.2.ce, faevo.2.cc), na.rm = T)) # match forward primer, reverse primer, and yeast strain BCs
## [1] "matching counts (match f and r primer barcode, and moby bc): 104365740"
paste0("analysis counts: ", sum(c(poc.ce, poc.cc, faevo.ce, faevo.cc), na.rm = T)) # expected and cc counts in the analysis data
## [1] "analysis counts: 84776627"
paste0("expected analysis counts: ", sum(c(poc.ce, faevo.ce), na.rm = T)) # expected counts in the analysis data
## [1] "expected analysis counts: 81780343"
paste0("median counts per barcode: ", median(c(poc.ce, faevo.ce), na.rm = T)) # median number of counts per barcode in the expected counts dataset.
## [1] "median counts per barcode: 2961"Analysis: Assess POC library barcode cross-contamination rate.
# calculations
# ----------------------------------------------------------------
cc = (((colSums(poc.cc, na.rm = T))/(colSums(poc.ce, na.rm = T) + colSums(poc.cc,
na.rm = T)))/colSums(!is.na(poc.cc))) * 100 # cc rate per exp
cc[is.nan(cc)] <- NA # NA nans
myt <- as.data.frame(cc) # convert to dataframe
myt$tc <- (colSums(poc.ce, na.rm = T) + colSums(poc.cc, na.rm = T)) # total counts to use as weight in weighted mean
cc001 <- weighted.mean(myt$cc, myt$tc, na.rm = T) # save weighted mean cc (by expected reads) for grand cc rate.
r001 <- mean(myt$tc) # save mean for below calculation of grand cc rate.
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- myt$cc
myr <- myt$tc
myc <- setpalette[1]
mybinwidth <- 0.01
mytitle <- NULL
myylab <- "Count"
myxlab <- "Mean Barcode Contamination Rate (Percent Total Counts)"
myhighlight <- "black"
myfillapha <- 0.35
myptalpha <- 0.35
mylinealpha <- 0.35
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
p_a <- ggplot(myt, aes(x = myx)) + geom_histogram(alpha = myfillapha, color = myc,
fill = myc, binwidth = mybinwidth) + geom_vline(xintercept = weighted.mean(myx,
myr, na.rm = T), linetype = "dashed", color = myhighlight) + geom_rug(color = myc,
alpha = myptalpha) + xlim(NA, 1) + theme_classic() + ggtitle(mytitle) +
ylab(myylab) + xlab(myxlab) + theme(axis.title.y = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = mytextsize,
angle = 0)) + theme(axis.text.x = element_text(size = mytextsize, angle = 0)) +
geom_hline(yintercept = -0.01, colour = "white", size = 1.01) + annotate("text",
x = 1, y = 47, label = "A", size = 10)
p_a
## Warning: Removed 20 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
# -----------------------------------------------------------------------------
rm(cc, myt) # remove the temporary variableAnalysis: Assess 250-generation experiment consensus library barcode cross-contamination rate.
# Calculations
# ----------------------------------------------------------------
cc = ((colSums(faevo.cc, na.rm = T))/(colSums(faevo.ce, na.rm = T) + colSums(faevo.cc,
na.rm = T)))/colSums(!is.na(faevo.cc)) * 100 # cc rate per exp
cc[is.nan(cc)] <- NA # NA nans
myt <- as.data.frame(cc) # convert to dataframe
myt$tc <- (colSums(faevo.ce, na.rm = T) + colSums(faevo.cc, na.rm = T)) # total counts to use as weight in weighted mean
cc002 <- weighted.mean(myt$cc, myt$tc, na.rm = T) # save weighted mean cc (by expected reads) for grand cc rate.
r002 <- mean(myt$tc) # save mean for below calculation of grand cc rate.
# overall cc rate
paste0("mean cross contam as % total counts = ", weighted.mean(c(cc001, cc002),
c(r001, r002)), " %") # weighted mean contam weighted.mean(c(contam rate poc, contam rate faevo), c(total counts poc, total counts faevo))
## [1] "mean cross contam as % total counts = 0.0427158666120247 %"
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx2 <- myt$cc
myr <- myt$tc
myc <- setpalette[1]
mybinwidth <- 0.01
mytitle <- NULL
myylab <- "Count"
myxlab <- "Mean Barcode Contamination Rate (Percent Total Counts)"
myhighlight <- "black"
myfillapha <- 0.35
myptalpha <- 0.35
mylinealpha <- 0.35
mytextsize <- 8
myheight <- 7
mywidth <- 7
p_b <- ggplot(myt, aes(x = myx2)) + geom_histogram(alpha = myfillapha, color = myc,
fill = myc, binwidth = mybinwidth) + geom_vline(xintercept = weighted.mean(myx2,
myr, na.rm = T), linetype = "dashed", color = myhighlight) + geom_rug(color = myc,
alpha = myptalpha) + xlim(NA, 1) + theme_classic() + ggtitle(mytitle) +
ylab(myylab) + xlab(myxlab) + theme(axis.title.y = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = mytextsize,
angle = 0)) + theme(axis.text.x = element_text(size = mytextsize, angle = 0)) +
geom_hline(yintercept = -0.01, colour = "white", size = 1.01) + annotate("text",
x = 1, y = 50, label = "B", size = 10)
p_b
## Warning: Removed 1 rows containing missing values (geom_bar).
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
grid.arrange(p_a, p_b, nrow = 2)
## Warning: Removed 20 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
## Warning: Removed 1 rows containing missing values (geom_bar).
pdf(file = "000_Additional_File_7.pdf", width = mywidth, height = myheight)
grid.arrange(p_a, p_b, nrow = 2)
## Warning: Removed 20 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
## Warning: Removed 1 rows containing missing values (geom_bar).
dev.off() # 2columns wide, half as tall
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(cc, myt, p_a, p_b, cc001, cc002, r001, r002, myx2, mybinwidth, myc, myfillapha,
myheight, myhighlight, mylinealpha, myptalpha, myr, mytextsize, mytitle,
mywidth, myx, myxlab, myylab) # remove the temporary variableAnalysis: Assess change in barcode cross-contamination rate for samples from the 250-generation experiment that were resequenced.
# Calculations & models
# -------------------------------------------------------
faevo.1.ce <- faevo.1.ce[, (colnames(faevo.1.ce) %in% colnames(faevo.2.ce))] # subset the run 1 data to only include those columns in the rerun dataset..expected counts..
faevo.1.cc <- faevo.1.cc[, (colnames(faevo.1.cc) %in% colnames(faevo.2.cc))] # cross contamination counts
faevo.1.ce <- faevo.1.ce[, colnames(faevo.1.ce)]
faevo.1.cc <- faevo.1.cc[, colnames(faevo.1.ce)] # reorder the run 1 data by column name
faevo.2.ce <- faevo.2.ce[, colnames(faevo.1.ce)]
faevo.2.cc <- faevo.2.cc[, colnames(faevo.1.ce)] # reorder the run 2 data by run 1 column name (for paired t test below)
myt1 <- ((colSums(faevo.1.cc, na.rm = T))/(colSums(faevo.1.ce, na.rm = T) +
colSums(faevo.1.cc, na.rm = T)))/colSums(!is.na(faevo.1.cc)) # cross contam run 1
myt2 <- ((colSums(faevo.2.cc, na.rm = T))/(colSums(faevo.2.ce, na.rm = T) +
colSums(faevo.2.cc, na.rm = T)))/colSums(!is.na(faevo.2.cc)) # cross contam run 2
myt1r <- (colSums(faevo.1.ce, na.rm = T) + colSums(faevo.1.cc, na.rm = T)) # reads run 1
myt2r <- (colSums(faevo.2.ce, na.rm = T) + colSums(faevo.2.cc, na.rm = T)) # reads run 2
myt <- myt1 - myt2 # calculatation for weighted t.test below (contam run 1 - contam run 2)
mytr <- 2/((1/myt1r) + (1/myt2r)) # reads for calculation below (harmonic mean of reads for run 1 and run 2)
mymod2 <- wtd.t.test(x = myt, y = 0, weight = mytr, alternative = "greater",
mean1 = T) # t.test, paired, 1 sided (greater), weighted
# -----------------------------------------------------------------------------
# create temporary plotting dataframe
# -----------------------------------------
myt1 <- as.data.frame(cbind(myt1, myt1r))
colnames(myt1) <- c("cc", "r")
myt1$seq.run <- 1 # run 1 df
myt2 <- as.data.frame(cbind(myt2, myt2r))
colnames(myt2) <- c("cc", "r")
myt2$seq.run <- 2 # run 2 df
myt <- rbind(myt1, myt2)
myt$seq.run <- as.factor(myt$seq.run) # combine
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- myt$seq.run
myy <- myt$cc
myr <- myt$r
myx1 <- myt1$cc
myr1 <- myt1$r
myx2 <- myt2$cc
myr2 <- myt2$r
mytitle <- NULL
myylab <- "Mean Barcode Contamination Rate (% Total Counts)"
myxlab <- "Sequenced Libary Run Number"
myoutline <- "grey50"
myfill <- "grey90"
myhighlight <- "grey50"
myfillapha <- 0.35
myptalpha <- 0.35
mylinealpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 7
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(myt, aes(x = myx, y = myy, weight = myr, color = myx)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_segment(x = 0.75, xend = 1.25, y = weighted.mean(myx1,
myr1), yend = weighted.mean(myx1, myr1), color = myhighlight) + geom_segment(x = 1.75,
xend = 2.25, y = weighted.mean(myx2, myr2), yend = weighted.mean(myx2, myr2),
color = myhighlight) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha,
size = myptsize) + geom_rug(sides = "l", alpha = myptalpha) + scale_color_manual(values = mypalette) +
geom_hline(yintercept = 0, lty = "dotted") + theme_classic() + theme(legend.position = "none") +
ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) + scale_x_discrete(breaks = c("1",
"2"), labels = c("Library 1", "Library 2")) + theme(axis.title.y = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = mytextsize,
angle = 0)) + theme(axis.text.x = element_text(size = mytextsize, angle = 0))
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_8.pdf", width = mywidth, height = myheight)
p
dev.off() # 7by7
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(faevo.1.ce, faevo.1.cc, faevo.2.ce, faevo.2.cc, myt1, myt1r, myt2, myt2r,
myt, mytr, p) # clean up this section.
rm(faevo.cc, faevo.ce, poc.cc, poc.ce) # clean up for next major set of analyses (remove the counts datasets to keep our global environment clean)
rm(myfill, myfillapha, myheight, myhighlight, mylinealpha, myoutline, mypalette,
myptalpha, myptsize, myr, myr1, myr2, mytextsize, mytitle, mywidth, myx,
myx1, myx2, myxlab, myy, myylab) # remove plotting paramsAnalysis: Assess fitness change in 250-generations of experimental evolution for barcodes in the 250-generation experiment.
my <- myfa[order(myfa$bctID), ]
# model & diagnostic plots
# ----------------------------------------------------
mymod <- lm(deltafit ~ bctID + 0, weights = rdeltafit, data = my)
summary(mymod)
##
## Call:
## lm(formula = deltafit ~ bctID + 0, data = my, weights = rdeltafit)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -8.9024 -0.9879 0.0734 0.9561 4.8229
##
## Coefficients:
## Estimate Std. Error t value
## bctIDCM+Ethanoldiploid0.001d1A2 0.00224666 0.01223756 0.184
## bctIDCM+Ethanoldiploid0.001d1A4 0.01765500 0.01365937 1.293
## bctIDCM+Ethanoldiploid0.001d1A5 -0.00183138 0.01601185 -0.114
## bctIDCM+Ethanoldiploid0.001d1A6 0.00847721 0.01160185 0.731
## bctIDCM+Ethanoldiploid0.001d1A7 0.00837054 0.01577339 0.531
## bctIDCM+Ethanoldiploid0.001d1D10 0.03354979 0.01054805 3.181
## bctIDCM+Ethanoldiploid0.001d1D11 0.02980938 0.01093362 2.726
## bctIDCM+Ethanoldiploid0.001d1D12 0.02399057 0.01018367 2.356
## bctIDCM+Ethanoldiploid0.001d1D9 0.02343344 0.02027118 1.156
## bctIDCM+Ethanoldiploid0.001d1E10 0.04807661 0.01120179 4.292
## bctIDCM+Ethanoldiploid0.001d1E11 0.03162314 0.01125755 2.809
## bctIDCM+Ethanoldiploid0.001d1E12 -0.03049231 0.01211023 -2.518
## bctIDCM+Ethanoldiploid0.001d1E9 -0.00999714 0.01070592 -0.934
## bctIDCM+Ethanoldiploid0.001d1F1 0.00938513 0.00879011 1.068
## bctIDCM+Ethanoldiploid0.001d1F2 0.01938389 0.01121524 1.728
## bctIDCM+Ethanoldiploid0.001d1G1 0.01657615 0.01500470 1.105
## bctIDCM+Ethanoldiploid0.001d1G2 -0.01127658 0.01818702 -0.620
## bctIDCM+Ethanoldiploid0.001d1H2 -0.01386016 0.01837215 -0.754
## bctIDCM+Ethanoldiploid0.001d1H4 0.00296372 0.02376768 0.125
## bctIDCM+Ethanoldiploid0.001d1H5 0.00347374 0.01381229 0.251
## bctIDCM+Ethanoldiploid0.001d1H6 0.00333766 0.01279047 0.261
## bctIDCM+Ethanoldiploid0.001d1H7 -0.00551949 0.02558074 -0.216
## bctIDCM+Saltdiploid0.001d1A2 0.04403600 0.01205900 3.652
## bctIDCM+Saltdiploid0.001d1A3 0.01686781 0.01180626 1.429
## bctIDCM+Saltdiploid0.001d1A4 0.02820342 0.01240104 2.274
## bctIDCM+Saltdiploid0.001d1A5 0.39852910 0.19667177 2.026
## bctIDCM+Saltdiploid0.001d1A7 0.02578701 0.01322947 1.949
## bctIDCM+Saltdiploid0.001d1F3 0.14079891 0.02033980 6.922
## bctIDCM+Saltdiploid0.001d1F4 0.18806044 0.02296431 8.189
## bctIDCM+Saltdiploid0.001d1F5 0.19913858 0.15799739 1.260
## bctIDCM+Saltdiploid0.001d1F6 0.23451078 0.01480773 15.837
## bctIDCM+Saltdiploid0.001d1F7 0.15255139 0.02121079 7.192
## bctIDCM+Saltdiploid0.001d1F8 0.12888704 0.01760927 7.319
## bctIDCM+Saltdiploid0.001d1G3 0.10101471 0.03918185 2.578
## bctIDCM+Saltdiploid0.001d1G4 0.13402619 0.04517263 2.967
## bctIDCM+Saltdiploid0.001d1G5 0.09585476 0.01936410 4.950
## bctIDCM+Saltdiploid0.001d1G6 0.08971402 0.03285475 2.731
## bctIDCM+Saltdiploid0.001d1G7 0.08740043 0.02106254 4.150
## bctIDCM+Saltdiploid0.001d1G8 0.12572385 0.03290001 3.821
## bctIDCM+Saltdiploid0.001d1H2 0.00495816 0.01539561 0.322
## bctIDCM+Saltdiploid0.001d1H3 0.02115633 0.01617586 1.308
## bctIDCM+Saltdiploid0.001d1H4 0.02137396 0.01932366 1.106
## bctIDCM+Saltdiploid0.001d1H5 0.00354951 0.01200179 0.296
## bctIDCM+Saltdiploid0.001d1H7 0.09417670 0.02624046 3.589
## bctIDCMdiploid0.00025d1A2 0.01559982 0.01599335 0.975
## bctIDCMdiploid0.00025d1A3 -0.00878718 0.01495776 -0.587
## bctIDCMdiploid0.00025d1A4 0.00174608 0.01441827 0.121
## bctIDCMdiploid0.00025d1A5 0.02677688 0.01511949 1.771
## bctIDCMdiploid0.00025d1A6 0.01198957 0.01303287 0.920
## bctIDCMdiploid0.00025d1A7 -0.00440271 0.01621492 -0.272
## bctIDCMdiploid0.00025d1B10 -0.00809898 0.01653299 -0.490
## bctIDCMdiploid0.00025d1B11 0.01723177 0.01242047 1.387
## bctIDCMdiploid0.00025d1B12 0.02983743 0.01830367 1.630
## bctIDCMdiploid0.00025d1B9 0.02155039 0.01336058 1.613
## bctIDCMdiploid0.00025d1C10 -0.01674094 0.01859239 -0.900
## bctIDCMdiploid0.00025d1C11 0.03216629 0.01622343 1.983
## bctIDCMdiploid0.00025d1C12 0.01496869 0.01326625 1.128
## bctIDCMdiploid0.00025d1C9 -0.00518047 0.01188006 -0.436
## bctIDCMdiploid0.00025d1D2 0.00370897 0.05550949 0.067
## bctIDCMdiploid0.00025d1E2 0.01289359 0.01052145 1.225
## bctIDCMdiploid0.00025d1H2 0.00760594 0.01762321 0.432
## bctIDCMdiploid0.00025d1H3 -0.00458899 0.01745253 -0.263
## bctIDCMdiploid0.00025d1H4 0.01843903 0.02426240 0.760
## bctIDCMdiploid0.00025d1H5 0.02541993 0.01377634 1.845
## bctIDCMdiploid0.00025d1H6 0.00414724 0.01352400 0.307
## bctIDCMdiploid0.00025d1H7 0.00190764 0.02782539 0.069
## bctIDCMdiploid0.001d1A11 0.03612225 0.01815435 1.990
## bctIDCMdiploid0.001d1A2 0.00809289 0.01417709 0.571
## bctIDCMdiploid0.001d1A2B 0.00927271 0.01214216 0.764
## bctIDCMdiploid0.001d1A3 0.00032174 0.01485237 0.022
## bctIDCMdiploid0.001d1A3B 0.00398055 0.01385717 0.287
## bctIDCMdiploid0.001d1A4 0.01780845 0.01310805 1.359
## bctIDCMdiploid0.001d1A4B 0.00153330 0.01338588 0.115
## bctIDCMdiploid0.001d1A5 0.02937872 0.01569201 1.872
## bctIDCMdiploid0.001d1A5B -0.00512256 0.01465871 -0.349
## bctIDCMdiploid0.001d1A6 0.02327336 0.01302785 1.786
## bctIDCMdiploid0.001d1A6B 0.00955064 0.01339667 0.713
## bctIDCMdiploid0.001d1A7 0.04290593 0.01344789 3.191
## bctIDCMdiploid0.001d1A8 0.01512575 0.01169949 1.293
## bctIDCMdiploid0.001d1A9 0.02268337 0.01128163 2.011
## bctIDCMdiploid0.001d1B1 0.05232713 0.01916650 2.730
## bctIDCMdiploid0.001d1B2 0.04063255 0.01363624 2.980
## bctIDCMdiploid0.001d1C1 0.05496896 0.01813523 3.031
## bctIDCMdiploid0.001d1C2 0.03217244 0.01299148 2.476
## bctIDCMdiploid0.001d1D4 0.08813997 0.05292837 1.665
## bctIDCMdiploid0.001d1D5 -0.01879019 0.01310839 -1.433
## bctIDCMdiploid0.001d1D6 0.04266468 0.04243456 1.005
## bctIDCMdiploid0.001d1D7 0.01221091 0.01202676 1.015
## bctIDCMdiploid0.001d1D8 -0.01270792 0.01081313 -1.175
## bctIDCMdiploid0.001d1E4 0.00301728 0.00782870 0.385
## bctIDCMdiploid0.001d1E5 0.03661495 0.02836404 1.291
## bctIDCMdiploid0.001d1E6 -0.02094510 0.00919326 -2.278
## bctIDCMdiploid0.001d1E7 0.02727065 0.01036588 2.631
## bctIDCMdiploid0.001d1E8 -0.00435115 0.01093102 -0.398
## bctIDCMdiploid0.001d1H11 0.03103102 0.01491899 2.080
## bctIDCMdiploid0.001d1H2 0.01207171 0.01797336 0.672
## bctIDCMdiploid0.001d1H2B -0.01771525 0.01722331 -1.029
## bctIDCMdiploid0.001d1H3 -0.00572467 0.01932551 -0.296
## bctIDCMdiploid0.001d1H3B 0.00004411 0.01824231 0.002
## bctIDCMdiploid0.001d1H4 0.00887392 0.02490203 0.356
## bctIDCMdiploid0.001d1H4B -0.00268735 0.02541084 -0.106
## bctIDCMdiploid0.001d1H5 0.01359003 0.01528546 0.889
## bctIDCMdiploid0.001d1H5B -0.00274906 0.01359686 -0.202
## bctIDCMdiploid0.001d1H6 0.03458717 0.01367055 2.530
## bctIDCMdiploid0.001d1H6B 0.02557412 0.01329414 1.924
## bctIDCMdiploid0.001d1H7 0.00942620 0.02647801 0.356
## bctIDCMdiploid0.001d1H8 0.01372130 0.02208970 0.621
## bctIDCMdiploid0.001d1H9 0.01922550 0.02317816 0.829
## bctIDCMdiploid0.004d1A2 0.00073845 0.01697648 0.043
## bctIDCMdiploid0.004d1A3 0.01219203 0.01903325 0.641
## bctIDCMdiploid0.004d1A4 0.05056340 0.01731766 2.920
## bctIDCMdiploid0.004d1A5 0.05149485 0.01798080 2.864
## bctIDCMdiploid0.004d1A6 0.01931859 0.01722893 1.121
## bctIDCMdiploid0.004d1A7 0.02865780 0.02004130 1.430
## bctIDCMdiploid0.004d1B3 0.02462782 0.01445493 1.704
## bctIDCMdiploid0.004d1B4 0.03125331 0.01659619 1.883
## bctIDCMdiploid0.004d1B5 0.04298949 0.01367318 3.144
## bctIDCMdiploid0.004d1B6 0.05327518 0.01738231 3.065
## bctIDCMdiploid0.004d1B7 0.07749931 0.01526527 5.077
## bctIDCMdiploid0.004d1C3 0.04273794 0.01469507 2.908
## bctIDCMdiploid0.004d1C4 0.02312320 0.01388803 1.665
## bctIDCMdiploid0.004d1C5 0.04334705 0.01293091 3.352
## bctIDCMdiploid0.004d1C6 0.06362223 0.01397915 4.551
## bctIDCMdiploid0.004d1C7 0.03835768 0.02896918 1.324
## bctIDCMdiploid0.004d1H2 0.02225147 0.02308198 0.964
## bctIDCMdiploid0.004d1H3 0.02106572 0.02173722 0.969
## bctIDCMdiploid0.004d1H4 0.00548235 0.03044485 0.180
## bctIDCMdiploid0.004d1H5 0.06026645 0.01911119 3.153
## bctIDCMdiploid0.004d1H6 0.04022208 0.01766259 2.277
## bctIDCMdiploid0.004d1H7 0.01164662 0.03155608 0.369
## bctIDCMhaploid0.001d1A2 0.17748276 0.18501465 0.959
## bctIDCMhaploid0.001d1A3 0.03475922 0.01642162 2.117
## bctIDCMhaploid0.001d1A3B 0.02653209 0.01137336 2.333
## bctIDCMhaploid0.001d1A4 0.07888998 0.01157424 6.816
## bctIDCMhaploid0.001d1A4B 0.00800659 0.01199416 0.668
## bctIDCMhaploid0.001d1A5 0.07567154 0.01371413 5.518
## bctIDCMhaploid0.001d1A5B 0.05376713 0.01259551 4.269
## bctIDCMhaploid0.001d1A6 0.02913551 0.01052784 2.767
## bctIDCMhaploid0.001d1A6B 0.02748147 0.00908708 3.024
## bctIDCMhaploid0.001d1A7 0.28424691 0.22971593 1.237
## bctIDCMhaploid0.001d1A7B 0.37549211 0.27832375 1.349
## bctIDCMhaploid0.001d1H2 0.05734718 0.01492184 3.843
## bctIDCMhaploid0.001d1H3 0.00588798 0.01683299 0.350
## bctIDCMhaploid0.001d1H3B 0.02271945 0.01846922 1.230
## bctIDCMhaploid0.001d1H4 0.06491278 0.03830520 1.695
## bctIDCMhaploid0.001d1H4B 0.01454690 0.01990335 0.731
## bctIDCMhaploid0.001d1H5 0.24925362 0.21028584 1.185
## bctIDCMhaploid0.001d1H5B 0.07768822 0.01659155 4.682
## bctIDCMhaploid0.001d1H6 0.10010351 0.02954796 3.388
## bctIDCMhaploid0.001d1H6B 0.11256605 0.02312772 4.867
## bctIDCMhaploid0.001d1H7 0.08865934 0.02084663 4.253
## bctIDCMhaploid0.001d1H7B 0.07939256 0.01932983 4.107
## Pr(>|t|)
## bctIDCM+Ethanoldiploid0.001d1A2 0.854419
## bctIDCM+Ethanoldiploid0.001d1A4 0.196832
## bctIDCM+Ethanoldiploid0.001d1A5 0.908990
## bctIDCM+Ethanoldiploid0.001d1A6 0.465352
## bctIDCM+Ethanoldiploid0.001d1A7 0.595903
## bctIDCM+Ethanoldiploid0.001d1D10 0.001570 **
## bctIDCM+Ethanoldiploid0.001d1D11 0.006650 **
## bctIDCM+Ethanoldiploid0.001d1D12 0.018906 *
## bctIDCM+Ethanoldiploid0.001d1D9 0.248287
## bctIDCM+Ethanoldiploid0.001d1E10 0.00002165288566661 ***
## bctIDCM+Ethanoldiploid0.001d1E11 0.005182 **
## bctIDCM+Ethanoldiploid0.001d1E12 0.012147 *
## bctIDCM+Ethanoldiploid0.001d1E9 0.350903
## bctIDCM+Ethanoldiploid0.001d1F1 0.286224
## bctIDCM+Ethanoldiploid0.001d1F2 0.084602 .
## bctIDCM+Ethanoldiploid0.001d1G1 0.269859
## bctIDCM+Ethanoldiploid0.001d1G2 0.535545
## bctIDCM+Ethanoldiploid0.001d1H2 0.450992
## bctIDCM+Ethanoldiploid0.001d1H4 0.900820
## bctIDCM+Ethanoldiploid0.001d1H5 0.801543
## bctIDCM+Ethanoldiploid0.001d1H6 0.794250
## bctIDCM+Ethanoldiploid0.001d1H7 0.829265
## bctIDCM+Saltdiploid0.001d1A2 0.000291 ***
## bctIDCM+Saltdiploid0.001d1A3 0.153770
## bctIDCM+Saltdiploid0.001d1A4 0.023413 *
## bctIDCM+Saltdiploid0.001d1A5 0.043309 *
## bctIDCM+Saltdiploid0.001d1A7 0.051883 .
## bctIDCM+Saltdiploid0.001d1F3 0.00000000001514729 ***
## bctIDCM+Saltdiploid0.001d1F4 0.00000000000000266 ***
## bctIDCM+Saltdiploid0.001d1F5 0.208173
## bctIDCM+Saltdiploid0.001d1F6 < 0.0000000000000002 ***
## bctIDCM+Saltdiploid0.001d1F7 0.00000000000263293 ***
## bctIDCM+Saltdiploid0.001d1F8 0.00000000000113446 ***
## bctIDCM+Saltdiploid0.001d1G3 0.010247 *
## bctIDCM+Saltdiploid0.001d1G4 0.003166 **
## bctIDCM+Saltdiploid0.001d1G5 0.00000104573824082 ***
## bctIDCM+Saltdiploid0.001d1G6 0.006566 **
## bctIDCM+Saltdiploid0.001d1G7 0.00003974952106726 ***
## bctIDCM+Saltdiploid0.001d1G8 0.000151 ***
## bctIDCM+Saltdiploid0.001d1H2 0.747562
## bctIDCM+Saltdiploid0.001d1H3 0.191568
## bctIDCM+Saltdiploid0.001d1H4 0.269265
## bctIDCM+Saltdiploid0.001d1H5 0.767557
## bctIDCM+Saltdiploid0.001d1H7 0.000368 ***
## bctIDCMdiploid0.00025d1A2 0.329882
## bctIDCMdiploid0.00025d1A3 0.557181
## bctIDCMdiploid0.00025d1A4 0.903664
## bctIDCMdiploid0.00025d1A5 0.077226 .
## bctIDCMdiploid0.00025d1A6 0.358086
## bctIDCMdiploid0.00025d1A7 0.786112
## bctIDCMdiploid0.00025d1B10 0.624463
## bctIDCMdiploid0.00025d1B11 0.166007
## bctIDCMdiploid0.00025d1B12 0.103764
## bctIDCMdiploid0.00025d1B9 0.107440
## bctIDCMdiploid0.00025d1C10 0.368373
## bctIDCMdiploid0.00025d1C11 0.048000 *
## bctIDCMdiploid0.00025d1C12 0.259774
## bctIDCMdiploid0.00025d1C9 0.662997
## bctIDCMdiploid0.00025d1D2 0.946757
## bctIDCMdiploid0.00025d1E2 0.221035
## bctIDCMdiploid0.00025d1H2 0.666246
## bctIDCMdiploid0.00025d1H3 0.792714
## bctIDCMdiploid0.00025d1H4 0.447657
## bctIDCMdiploid0.00025d1H5 0.065659 .
## bctIDCMdiploid0.00025d1H6 0.759244
## bctIDCMdiploid0.00025d1H7 0.945372
## bctIDCMdiploid0.001d1A11 0.047218 *
## bctIDCMdiploid0.001d1A2 0.568387
## bctIDCMdiploid0.001d1A2B 0.445453
## bctIDCMdiploid0.001d1A3 0.982727
## bctIDCMdiploid0.001d1A3B 0.774047
## bctIDCMdiploid0.001d1A4 0.174949
## bctIDCMdiploid0.001d1A4B 0.908856
## bctIDCMdiploid0.001d1A5 0.061817 .
## bctIDCMdiploid0.001d1A5B 0.726909
## bctIDCMdiploid0.001d1A6 0.074694 .
## bctIDCMdiploid0.001d1A6B 0.476265
## bctIDCMdiploid0.001d1A7 0.001518 **
## bctIDCMdiploid0.001d1A8 0.196716
## bctIDCMdiploid0.001d1A9 0.044951 *
## bctIDCMdiploid0.001d1B1 0.006576 **
## bctIDCMdiploid0.001d1B2 0.003039 **
## bctIDCMdiploid0.001d1C1 0.002576 **
## bctIDCMdiploid0.001d1C2 0.013632 *
## bctIDCMdiploid0.001d1D4 0.096546 .
## bctIDCMdiploid0.001d1D5 0.152415
## bctIDCMdiploid0.001d1D6 0.315226
## bctIDCMdiploid0.001d1D7 0.310496
## bctIDCMdiploid0.001d1D8 0.240516
## bctIDCMdiploid0.001d1E4 0.700111
## bctIDCMdiploid0.001d1E5 0.197395
## bctIDCMdiploid0.001d1E6 0.023170 *
## bctIDCMdiploid0.001d1E7 0.008807 **
## bctIDCMdiploid0.001d1E8 0.690775
## bctIDCMdiploid0.001d1H11 0.038086 *
## bctIDCMdiploid0.001d1H2 0.502150
## bctIDCMdiploid0.001d1H2B 0.304231
## bctIDCMdiploid0.001d1H3 0.767194
## bctIDCMdiploid0.001d1H3B 0.998072
## bctIDCMdiploid0.001d1H4 0.721741
## bctIDCMdiploid0.001d1H4B 0.915822
## bctIDCMdiploid0.001d1H5 0.374428
## bctIDCMdiploid0.001d1H5B 0.839864
## bctIDCMdiploid0.001d1H6 0.011740 *
## bctIDCMdiploid0.001d1H6B 0.055013 .
## bctIDCMdiploid0.001d1H7 0.722004
## bctIDCMdiploid0.001d1H8 0.534803
## bctIDCMdiploid0.001d1H9 0.407275
## bctIDCMdiploid0.004d1A2 0.965323
## bctIDCMdiploid0.004d1A3 0.522127
## bctIDCMdiploid0.004d1A4 0.003677 **
## bctIDCMdiploid0.004d1A5 0.004378 **
## bctIDCMdiploid0.004d1A6 0.262755
## bctIDCMdiploid0.004d1A7 0.153420
## bctIDCMdiploid0.004d1B3 0.089106 .
## bctIDCMdiploid0.004d1B4 0.060315 .
## bctIDCMdiploid0.004d1B5 0.001775 **
## bctIDCMdiploid0.004d1B6 0.002307 **
## bctIDCMdiploid0.004d1B7 0.00000056011850773 ***
## bctIDCMdiploid0.004d1C3 0.003811 **
## bctIDCMdiploid0.004d1C4 0.096605 .
## bctIDCMdiploid0.004d1C5 0.000868 ***
## bctIDCMdiploid0.004d1C6 0.00000684943418923 ***
## bctIDCMdiploid0.004d1C7 0.186138
## bctIDCMdiploid0.004d1H2 0.335547
## bctIDCMdiploid0.004d1H3 0.333005
## bctIDCMdiploid0.004d1H4 0.857174
## bctIDCMdiploid0.004d1H5 0.001720 **
## bctIDCMdiploid0.004d1H6 0.023234 *
## bctIDCMdiploid0.004d1H7 0.712242
## bctIDCMhaploid0.001d1A2 0.337921
## bctIDCMhaploid0.001d1A3 0.034829 *
## bctIDCMhaploid0.001d1A3B 0.020091 *
## bctIDCMhaploid0.001d1A4 0.00000000002976149 ***
## bctIDCMhaploid0.001d1A4B 0.504764
## bctIDCMhaploid0.001d1A5 0.00000005766073013 ***
## bctIDCMhaploid0.001d1A5B 0.00002392620167394 ***
## bctIDCMhaploid0.001d1A6 0.005879 **
## bctIDCMhaploid0.001d1A6B 0.002633 **
## bctIDCMhaploid0.001d1A7 0.216581
## bctIDCMhaploid0.001d1A7B 0.177968
## bctIDCMhaploid0.001d1H2 0.000139 ***
## bctIDCMhaploid0.001d1H3 0.726659
## bctIDCMhaploid0.001d1H3B 0.219285
## bctIDCMhaploid0.001d1H4 0.090830 .
## bctIDCMhaploid0.001d1H4B 0.465230
## bctIDCMhaploid0.001d1H5 0.236513
## bctIDCMhaploid0.001d1H5B 0.00000374585588773 ***
## bctIDCMhaploid0.001d1H6 0.000766 ***
## bctIDCMhaploid0.001d1H6B 0.00000156280464386 ***
## bctIDCMhaploid0.001d1H7 0.00002561158235811 ***
## bctIDCMhaploid0.001d1H7B 0.00004746151175424 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.922 on 456 degrees of freedom
## Multiple R-squared: 0.7186, Adjusted R-squared: 0.6248
## F-statistic: 7.66 on 152 and 456 DF, p-value: < 0.00000000000000022
hist(residuals(mymod), breaks = 100) # tails do look a little heavy
hist(expand_residuals(mymod, my$rdeltafit), breaks = 100) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here. 
plot(mymod) # model fit (multi) -- looks pretty good. 
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_12.html") # table does not have corrected p-values. | Â | deltafit | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| CM+Ethanoldiploid 0.001 d 1 A 2 | 0.00225 | -0.02174 – 0.02623 | 0.18359 | 0.85441888 |
| CM+Ethanoldiploid 0.001 d 1 A 4 | 0.01765 | -0.00912 – 0.04443 | 1.29252 | 0.19683180 |
| CM+Ethanoldiploid 0.001 d 1 A 5 | -0.00183 | -0.03321 – 0.02955 | -0.11438 | 0.90898955 |
| CM+Ethanoldiploid 0.001 d 1 A 6 | 0.00848 | -0.01426 – 0.03122 | 0.73068 | 0.46535168 |
| CM+Ethanoldiploid 0.001 d 1 A 7 | 0.00837 | -0.02254 – 0.03929 | 0.53067 | 0.59590266 |
| CM+Ethanoldiploid 0.001 d 1 D 10 | 0.03355 | 0.01288 – 0.05422 | 3.18066 | 0.00156959 |
| CM+Ethanoldiploid 0.001 d 1 D 11 | 0.02981 | 0.00838 – 0.05124 | 2.72640 | 0.00664970 |
| CM+Ethanoldiploid 0.001 d 1 D 12 | 0.02399 | 0.00403 – 0.04395 | 2.35579 | 0.01890587 |
| CM+Ethanoldiploid 0.001 d 1 D 9 | 0.02343 | -0.01630 – 0.06316 | 1.15600 | 0.24828733 |
| CM+Ethanoldiploid 0.001 d 1 E 10 | 0.04808 | 0.02612 – 0.07003 | 4.29187 | 0.00002165 |
| CM+Ethanoldiploid 0.001 d 1 E 11 | 0.03162 | 0.00956 – 0.05369 | 2.80906 | 0.00518210 |
| CM+Ethanoldiploid 0.001 d 1 E 12 | -0.03049 | -0.05423 – -0.00676 | -2.51790 | 0.01214729 |
| CM+Ethanoldiploid 0.001 d 1 E 9 | -0.01000 | -0.03098 – 0.01099 | -0.93380 | 0.35090332 |
| CM+Ethanoldiploid 0.001 d 1 F 1 | 0.00939 | -0.00784 – 0.02661 | 1.06769 | 0.28622445 |
| CM+Ethanoldiploid 0.001 d 1 F 2 | 0.01938 | -0.00260 – 0.04137 | 1.72835 | 0.08460206 |
| CM+Ethanoldiploid 0.001 d 1 G 1 | 0.01658 | -0.01283 – 0.04598 | 1.10473 | 0.26985901 |
| CM+Ethanoldiploid 0.001 d 1 G 2 | -0.01128 | -0.04692 – 0.02437 | -0.62003 | 0.53554478 |
| CM+Ethanoldiploid 0.001 d 1 H 2 | -0.01386 | -0.04987 – 0.02215 | -0.75441 | 0.45099161 |
| CM+Ethanoldiploid 0.001 d 1 H 4 | 0.00296 | -0.04362 – 0.04955 | 0.12470 | 0.90081956 |
| CM+Ethanoldiploid 0.001 d 1 H 5 | 0.00347 | -0.02360 – 0.03055 | 0.25150 | 0.80154341 |
| CM+Ethanoldiploid 0.001 d 1 H 6 | 0.00334 | -0.02173 – 0.02841 | 0.26095 | 0.79424982 |
| CM+Ethanoldiploid 0.001 d 1 H 7 | -0.00552 | -0.05566 – 0.04462 | -0.21577 | 0.82926550 |
| CM+Saltdiploid 0.001 d 1 A 2 | 0.04404 | 0.02040 – 0.06767 | 3.65171 | 0.00029070 |
| CM+Saltdiploid 0.001 d 1 A 3 | 0.01687 | -0.00627 – 0.04001 | 1.42872 | 0.15376998 |
| CM+Saltdiploid 0.001 d 1 A 4 | 0.02820 | 0.00390 – 0.05251 | 2.27428 | 0.02341305 |
| CM+Saltdiploid 0.001 d 1 A 5 | 0.39853 | 0.01306 – 0.78400 | 2.02637 | 0.04330880 |
| CM+Saltdiploid 0.001 d 1 A 7 | 0.02579 | -0.00014 – 0.05172 | 1.94921 | 0.05188310 |
| CM+Saltdiploid 0.001 d 1 F 3 | 0.14080 | 0.10093 – 0.18066 | 6.92234 | <0.001 |
| CM+Saltdiploid 0.001 d 1 F 4 | 0.18806 | 0.14305 – 0.23307 | 8.18925 | <0.001 |
| CM+Saltdiploid 0.001 d 1 F 5 | 0.19914 | -0.11053 – 0.50881 | 1.26039 | 0.20817273 |
| CM+Saltdiploid 0.001 d 1 F 6 | 0.23451 | 0.20549 – 0.26353 | 15.83705 | <0.001 |
| CM+Saltdiploid 0.001 d 1 F 7 | 0.15255 | 0.11098 – 0.19412 | 7.19216 | <0.001 |
| CM+Saltdiploid 0.001 d 1 F 8 | 0.12889 | 0.09437 – 0.16340 | 7.31927 | <0.001 |
| CM+Saltdiploid 0.001 d 1 G 3 | 0.10101 | 0.02422 – 0.17781 | 2.57810 | 0.01024726 |
| CM+Saltdiploid 0.001 d 1 G 4 | 0.13403 | 0.04549 – 0.22256 | 2.96698 | 0.00316552 |
| CM+Saltdiploid 0.001 d 1 G 5 | 0.09585 | 0.05790 – 0.13381 | 4.95013 | 0.00000105 |
| CM+Saltdiploid 0.001 d 1 G 6 | 0.08971 | 0.02532 – 0.15411 | 2.73063 | 0.00656636 |
| CM+Saltdiploid 0.001 d 1 G 7 | 0.08740 | 0.04612 – 0.12868 | 4.14957 | 0.00003975 |
| CM+Saltdiploid 0.001 d 1 G 8 | 0.12572 | 0.06124 – 0.19021 | 3.82139 | 0.00015111 |
| CM+Saltdiploid 0.001 d 1 H 2 | 0.00496 | -0.02522 – 0.03513 | 0.32205 | 0.74756239 |
| CM+Saltdiploid 0.001 d 1 H 3 | 0.02116 | -0.01055 – 0.05286 | 1.30790 | 0.19156762 |
| CM+Saltdiploid 0.001 d 1 H 4 | 0.02137 | -0.01650 – 0.05925 | 1.10610 | 0.26926505 |
| CM+Saltdiploid 0.001 d 1 H 5 | 0.00355 | -0.01997 – 0.02707 | 0.29575 | 0.76755679 |
| CM+Saltdiploid 0.001 d 1 H 7 | 0.09418 | 0.04275 – 0.14561 | 3.58899 | 0.00036794 |
| C Mdiploid 0.00025 d 1 A 2 | 0.01560 | -0.01575 – 0.04695 | 0.97539 | 0.32988206 |
| C Mdiploid 0.00025 d 1 A 3 | -0.00879 | -0.03810 – 0.02053 | -0.58747 | 0.55718131 |
| C Mdiploid 0.00025 d 1 A 4 | 0.00175 | -0.02651 – 0.03001 | 0.12110 | 0.90366385 |
| C Mdiploid 0.00025 d 1 A 5 | 0.02678 | -0.00286 – 0.05641 | 1.77102 | 0.07722576 |
| C Mdiploid 0.00025 d 1 A 6 | 0.01199 | -0.01355 – 0.03753 | 0.91995 | 0.35808612 |
| C Mdiploid 0.00025 d 1 A 7 | -0.00440 | -0.03618 – 0.02738 | -0.27152 | 0.78611237 |
| C Mdiploid 0.00025 d 1 B 10 | -0.00810 | -0.04050 – 0.02431 | -0.48987 | 0.62446295 |
| C Mdiploid 0.00025 d 1 B 11 | 0.01723 | -0.00711 – 0.04158 | 1.38737 | 0.16600725 |
| C Mdiploid 0.00025 d 1 B 12 | 0.02984 | -0.00604 – 0.06571 | 1.63013 | 0.10376368 |
| C Mdiploid 0.00025 d 1 B 9 | 0.02155 | -0.00464 – 0.04774 | 1.61298 | 0.10744003 |
| C Mdiploid 0.00025 d 1 C 10 | -0.01674 | -0.05318 – 0.01970 | -0.90042 | 0.36837253 |
| C Mdiploid 0.00025 d 1 C 11 | 0.03217 | 0.00037 – 0.06396 | 1.98271 | 0.04799999 |
| C Mdiploid 0.00025 d 1 C 12 | 0.01497 | -0.01103 – 0.04097 | 1.12833 | 0.25977419 |
| C Mdiploid 0.00025 d 1 C 9 | -0.00518 | -0.02846 – 0.01810 | -0.43606 | 0.66299683 |
| C Mdiploid 0.00025 d 1 D 2 | 0.00371 | -0.10509 – 0.11251 | 0.06682 | 0.94675682 |
| C Mdiploid 0.00025 d 1 E 2 | 0.01289 | -0.00773 – 0.03352 | 1.22546 | 0.22103521 |
| C Mdiploid 0.00025 d 1 H 2 | 0.00761 | -0.02693 – 0.04215 | 0.43159 | 0.66624601 |
| C Mdiploid 0.00025 d 1 H 3 | -0.00459 | -0.03880 – 0.02962 | -0.26294 | 0.79271446 |
| C Mdiploid 0.00025 d 1 H 4 | 0.01844 | -0.02911 – 0.06599 | 0.75998 | 0.44765688 |
| C Mdiploid 0.00025 d 1 H 5 | 0.02542 | -0.00158 – 0.05242 | 1.84519 | 0.06565851 |
| C Mdiploid 0.00025 d 1 H 6 | 0.00415 | -0.02236 – 0.03065 | 0.30666 | 0.75924362 |
| C Mdiploid 0.00025 d 1 H 7 | 0.00191 | -0.05263 – 0.05644 | 0.06856 | 0.94537182 |
| C Mdiploid 0.001 d 1 A 11 | 0.03612 | 0.00054 – 0.07170 | 1.98973 | 0.04721759 |
| C Mdiploid 0.001 d 1 A 2 | 0.00809 | -0.01969 – 0.03588 | 0.57084 | 0.56838732 |
| C Mdiploid 0.001 d 1 A 2 B | 0.00927 | -0.01453 – 0.03307 | 0.76368 | 0.44545327 |
| C Mdiploid 0.001 d 1 A 3 | 0.00032 | -0.02879 – 0.02943 | 0.02166 | 0.98272673 |
| C Mdiploid 0.001 d 1 A 3 B | 0.00398 | -0.02318 – 0.03114 | 0.28726 | 0.77404701 |
| C Mdiploid 0.001 d 1 A 4 | 0.01781 | -0.00788 – 0.04350 | 1.35859 | 0.17494851 |
| C Mdiploid 0.001 d 1 A 4 B | 0.00153 | -0.02470 – 0.02777 | 0.11455 | 0.90885560 |
| C Mdiploid 0.001 d 1 A 5 | 0.02938 | -0.00138 – 0.06013 | 1.87221 | 0.06181742 |
| C Mdiploid 0.001 d 1 A 5 B | -0.00512 | -0.03385 – 0.02361 | -0.34945 | 0.72690929 |
| C Mdiploid 0.001 d 1 A 6 | 0.02327 | -0.00226 – 0.04881 | 1.78643 | 0.07469382 |
| C Mdiploid 0.001 d 1 A 6 B | 0.00955 | -0.01671 – 0.03581 | 0.71291 | 0.47626537 |
| C Mdiploid 0.001 d 1 A 7 | 0.04291 | 0.01655 – 0.06926 | 3.19053 | 0.00151809 |
| C Mdiploid 0.001 d 1 A 8 | 0.01513 | -0.00780 – 0.03806 | 1.29286 | 0.19671558 |
| C Mdiploid 0.001 d 1 A 9 | 0.02268 | 0.00057 – 0.04479 | 2.01065 | 0.04495102 |
| C Mdiploid 0.001 d 1 B 1 | 0.05233 | 0.01476 – 0.08989 | 2.73013 | 0.00657598 |
| C Mdiploid 0.001 d 1 B 2 | 0.04063 | 0.01391 – 0.06736 | 2.97975 | 0.00303894 |
| C Mdiploid 0.001 d 1 C 1 | 0.05497 | 0.01942 – 0.09051 | 3.03106 | 0.00257568 |
| C Mdiploid 0.001 d 1 C 2 | 0.03217 | 0.00671 – 0.05764 | 2.47643 | 0.01363235 |
| C Mdiploid 0.001 d 1 D 4 | 0.08814 | -0.01560 – 0.19188 | 1.66527 | 0.09654594 |
| C Mdiploid 0.001 d 1 D 5 | -0.01879 | -0.04448 – 0.00690 | -1.43345 | 0.15241540 |
| C Mdiploid 0.001 d 1 D 6 | 0.04266 | -0.04051 – 0.12583 | 1.00542 | 0.31522648 |
| C Mdiploid 0.001 d 1 D 7 | 0.01221 | -0.01136 – 0.03578 | 1.01531 | 0.31049575 |
| C Mdiploid 0.001 d 1 D 8 | -0.01271 | -0.03390 – 0.00849 | -1.17523 | 0.24051605 |
| C Mdiploid 0.001 d 1 E 4 | 0.00302 | -0.01233 – 0.01836 | 0.38541 | 0.70011148 |
| C Mdiploid 0.001 d 1 E 5 | 0.03661 | -0.01898 – 0.09221 | 1.29089 | 0.19739471 |
| C Mdiploid 0.001 d 1 E 6 | -0.02095 | -0.03896 – -0.00293 | -2.27831 | 0.02316980 |
| C Mdiploid 0.001 d 1 E 7 | 0.02727 | 0.00695 – 0.04759 | 2.63081 | 0.00880654 |
| C Mdiploid 0.001 d 1 E 8 | -0.00435 | -0.02578 – 0.01707 | -0.39806 | 0.69077548 |
| C Mdiploid 0.001 d 1 H 11 | 0.03103 | 0.00179 – 0.06027 | 2.07997 | 0.03808645 |
| C Mdiploid 0.001 d 1 H 2 | 0.01207 | -0.02316 – 0.04730 | 0.67164 | 0.50215015 |
| C Mdiploid 0.001 d 1 H 2 B | -0.01772 | -0.05147 – 0.01604 | -1.02856 | 0.30423060 |
| C Mdiploid 0.001 d 1 H 3 | -0.00572 | -0.04360 – 0.03215 | -0.29622 | 0.76719449 |
| C Mdiploid 0.001 d 1 H 3 B | 0.00004 | -0.03571 – 0.03580 | 0.00242 | 0.99807165 |
| C Mdiploid 0.001 d 1 H 4 | 0.00887 | -0.03993 – 0.05768 | 0.35635 | 0.72174082 |
| C Mdiploid 0.001 d 1 H 4 B | -0.00269 | -0.05249 – 0.04712 | -0.10576 | 0.91582228 |
| C Mdiploid 0.001 d 1 H 5 | 0.01359 | -0.01637 – 0.04355 | 0.88908 | 0.37442760 |
| C Mdiploid 0.001 d 1 H 5 B | -0.00275 | -0.02940 – 0.02390 | -0.20218 | 0.83986354 |
| C Mdiploid 0.001 d 1 H 6 | 0.03459 | 0.00779 – 0.06138 | 2.53005 | 0.01174027 |
| C Mdiploid 0.001 d 1 H 6 B | 0.02557 | -0.00048 – 0.05163 | 1.92371 | 0.05501270 |
| C Mdiploid 0.001 d 1 H 7 | 0.00943 | -0.04247 – 0.06132 | 0.35600 | 0.72200440 |
| C Mdiploid 0.001 d 1 H 8 | 0.01372 | -0.02957 – 0.05702 | 0.62116 | 0.53480274 |
| C Mdiploid 0.001 d 1 H 9 | 0.01923 | -0.02620 – 0.06465 | 0.82947 | 0.40727462 |
| C Mdiploid 0.004 d 1 A 2 | 0.00074 | -0.03253 – 0.03401 | 0.04350 | 0.96532350 |
| C Mdiploid 0.004 d 1 A 3 | 0.01219 | -0.02511 – 0.04950 | 0.64056 | 0.52212714 |
| C Mdiploid 0.004 d 1 A 4 | 0.05056 | 0.01662 – 0.08451 | 2.91976 | 0.00367654 |
| C Mdiploid 0.004 d 1 A 5 | 0.05149 | 0.01625 – 0.08674 | 2.86388 | 0.00437789 |
| C Mdiploid 0.004 d 1 A 6 | 0.01932 | -0.01445 – 0.05309 | 1.12129 | 0.26275545 |
| C Mdiploid 0.004 d 1 A 7 | 0.02866 | -0.01062 – 0.06794 | 1.42994 | 0.15341977 |
| C Mdiploid 0.004 d 1 B 3 | 0.02463 | -0.00370 – 0.05296 | 1.70377 | 0.08910621 |
| C Mdiploid 0.004 d 1 B 4 | 0.03125 | -0.00127 – 0.06378 | 1.88316 | 0.06031462 |
| C Mdiploid 0.004 d 1 B 5 | 0.04299 | 0.01619 – 0.06979 | 3.14407 | 0.00177490 |
| C Mdiploid 0.004 d 1 B 6 | 0.05328 | 0.01921 – 0.08734 | 3.06491 | 0.00230654 |
| C Mdiploid 0.004 d 1 B 7 | 0.07750 | 0.04758 – 0.10742 | 5.07684 | 0.00000056 |
| C Mdiploid 0.004 d 1 C 3 | 0.04274 | 0.01394 – 0.07154 | 2.90832 | 0.00381118 |
| C Mdiploid 0.004 d 1 C 4 | 0.02312 | -0.00410 – 0.05034 | 1.66497 | 0.09660494 |
| C Mdiploid 0.004 d 1 C 5 | 0.04335 | 0.01800 – 0.06869 | 3.35220 | 0.00086846 |
| C Mdiploid 0.004 d 1 C 6 | 0.06362 | 0.03622 – 0.09102 | 4.55122 | 0.00000685 |
| C Mdiploid 0.004 d 1 C 7 | 0.03836 | -0.01842 – 0.09514 | 1.32409 | 0.18613776 |
| C Mdiploid 0.004 d 1 H 2 | 0.02225 | -0.02299 – 0.06749 | 0.96402 | 0.33554719 |
| C Mdiploid 0.004 d 1 H 3 | 0.02107 | -0.02154 – 0.06367 | 0.96911 | 0.33300485 |
| C Mdiploid 0.004 d 1 H 4 | 0.00548 | -0.05419 – 0.06515 | 0.18007 | 0.85717391 |
| C Mdiploid 0.004 d 1 H 5 | 0.06027 | 0.02281 – 0.09772 | 3.15346 | 0.00171997 |
| C Mdiploid 0.004 d 1 H 6 | 0.04022 | 0.00560 – 0.07484 | 2.27725 | 0.02323376 |
| C Mdiploid 0.004 d 1 H 7 | 0.01165 | -0.05020 – 0.07350 | 0.36908 | 0.71224172 |
| C Mhaploid 0.001 d 1 A 2 | 0.17748 | -0.18514 – 0.54010 | 0.95929 | 0.33792087 |
| C Mhaploid 0.001 d 1 A 3 | 0.03476 | 0.00257 – 0.06694 | 2.11667 | 0.03482853 |
| C Mhaploid 0.001 d 1 A 3 B | 0.02653 | 0.00424 – 0.04882 | 2.33283 | 0.02009125 |
| C Mhaploid 0.001 d 1 A 4 | 0.07889 | 0.05620 – 0.10158 | 6.81600 | <0.001 |
| C Mhaploid 0.001 d 1 A 4 B | 0.00801 | -0.01550 – 0.03151 | 0.66754 | 0.50476447 |
| C Mhaploid 0.001 d 1 A 5 | 0.07567 | 0.04879 – 0.10255 | 5.51778 | 0.00000006 |
| C Mhaploid 0.001 d 1 A 5 B | 0.05377 | 0.02908 – 0.07845 | 4.26875 | 0.00002393 |
| C Mhaploid 0.001 d 1 A 6 | 0.02914 | 0.00850 – 0.04977 | 2.76747 | 0.00587909 |
| C Mhaploid 0.001 d 1 A 6 B | 0.02748 | 0.00967 – 0.04529 | 3.02424 | 0.00263330 |
| C Mhaploid 0.001 d 1 A 7 | 0.28425 | -0.16599 – 0.73448 | 1.23738 | 0.21658119 |
| C Mhaploid 0.001 d 1 A 7 B | 0.37549 | -0.17001 – 0.92100 | 1.34912 | 0.17796797 |
| C Mhaploid 0.001 d 1 H 2 | 0.05735 | 0.02810 – 0.08659 | 3.84317 | 0.00013869 |
| C Mhaploid 0.001 d 1 H 3 | 0.00589 | -0.02710 – 0.03888 | 0.34979 | 0.72665899 |
| C Mhaploid 0.001 d 1 H 3 B | 0.02272 | -0.01348 – 0.05892 | 1.23012 | 0.21928455 |
| C Mhaploid 0.001 d 1 H 4 | 0.06491 | -0.01016 – 0.13999 | 1.69462 | 0.09083022 |
| C Mhaploid 0.001 d 1 H 4 B | 0.01455 | -0.02446 – 0.05356 | 0.73088 | 0.46522961 |
| C Mhaploid 0.001 d 1 H 5 | 0.24925 | -0.16290 – 0.66141 | 1.18531 | 0.23651278 |
| C Mhaploid 0.001 d 1 H 5 B | 0.07769 | 0.04517 – 0.11021 | 4.68240 | 0.00000375 |
| C Mhaploid 0.001 d 1 H 6 | 0.10010 | 0.04219 – 0.15802 | 3.38783 | 0.00076555 |
| C Mhaploid 0.001 d 1 H 6 B | 0.11257 | 0.06724 – 0.15790 | 4.86715 | 0.00000156 |
| C Mhaploid 0.001 d 1 H 7 | 0.08866 | 0.04780 – 0.12952 | 4.25293 | 0.00002561 |
| C Mhaploid 0.001 d 1 H 7 B | 0.07939 | 0.04151 – 0.11728 | 4.10726 | 0.00004746 |
| Observations | 608 | |||
| R2 / R2 adjusted | 0.719 / 0.625 | |||
# -----------------------------------------------------------------------------
# create results dataframe
# ----------------------------------------------------
myres <- as.data.frame(coef(summary(mymod)), stringsAsFactors = F)
myres$p1s_l <- pt(coef(summary(mymod))[, 3], mymod$df, lower = T) # one-sided test p-values for lower
myres$p1s_h <- pt(coef(summary(mymod))[, 3], mymod$df, lower = F) # one-sided test p-values for higher
myres$p1s_l.adj <- p.adjust(myres$p1s_l, method = "fdr") # fdr adjusted p value (for 1-sided lower)
myres$p1s_h.adj <- p.adjust(myres$p1s_h, method = "fdr") # fdr adjusted p value (for 1-sided higher)
myres$bcid <- gsub("^.*?D", "", rownames(myres)) # fix barcode id names
myres$m_deltafit <- ddply(my, ~bctID, function(my) weighted.mean(my$deltafit,
my$rdeltafit))[, 2]
myres$m_rdeltafit <- ddply(my, ~bctID, function(my) mean(my$rdeltafit))[, 2]
myres$mcm_deltafit <- myres$m_deltafit - weighted.mean(myres$m_deltafit, myres$m_rdeltafit)
myres$se_deltafit <- ddply(my, ~bctID, function(my) sjstats::se(my$deltafit))[,
2]
myres$color <- "fdr > 0.01"
myres$color[myres$p1s_h.adj <= 0.01] <- "fdr <= 0.01"
myres$color <- as.factor(myres$color)
myres$color <- relevel(myres$color, "fdr > 0.01")
myres$treatment <- gsub("0025d", "0025", gsub("001d", "001", gsub(".{4}$", "",
myres$bcid))) # strip bc id from treatment label, store in myres
# -----------------------------------------------------------------------------
# review results
# --------------------------------------------------------------
paste0("weighted mean fitness increase for increasers = ", weighted.mean(myres$m_deltafit[myres$p1s_h.adj <=
0.01], myres$m_rdeltafit[myres$p1s_h.adj <= 0.01])) # weighted mean fit inc of sig increasers only
## [1] "weighted mean fitness increase for increasers = 0.0679894955523455"
paste0("minimum fitness increase for increasers = ", min(myres$m_deltafit[myres$p1s_h.adj <=
0.01])) # minimum significant increase of 2.4%
## [1] "minimum fitness increase for increasers = 0.0274814710527647"
paste0("maximum fitness increase for increasers = ", max(myres$m_deltafit[myres$p1s_h.adj <=
0.01])) # maximum significant increase of 23.5%
## [1] "maximum fitness increase for increasers = 0.234510778858253"
# all treatments together
paste0("Number of barcodes with significant decreases in fitness = ", nrow(myres[myres$p1s_l.adj <=
0.01, ])) # check for significant fitness decrease: # find none
## [1] "Number of barcodes with significant decreases in fitness = 0"
paste0("Number of barcodes with significant increases in fitness = ", nrow(myres[myres$p1s_h.adj <=
0.01, ])) # check for significant fitness increase: # find 35 rows. (35 increasers out of 152)
## [1] "Number of barcodes with significant increases in fitness = 35"
paste0("Proportion of barcodes with significant increases in fitness = ", nrow(myres[myres$p1s_h.adj <=
0.01, ])/nrow(myres)) # prop increasers # 0.23
## [1] "Proportion of barcodes with significant increases in fitness = 0.230263157894737"
# CM diploid 1:1000
paste0("CM diploid 1:1000:")
## [1] "CM diploid 1:1000:"
myrest <- myres[myres$treatment == "CMdiploid0.001", ]
paste0("n barcodes in treatment = ", nrow(myrest))
## [1] "n barcodes in treatment = 42"
paste0("n barcodes with fitness increase in treatment = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ]))
## [1] "n barcodes with fitness increase in treatment = 3"
paste0("proportion of barcodes in treatment that increased in fitness = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ])/nrow(myrest))
## [1] "proportion of barcodes in treatment that increased in fitness = 0.0714285714285714"
# CM haploid 1:1000
paste0("CM haploid 1:1000:")
## [1] "CM haploid 1:1000:"
myrest <- myres[myres$treatment == "CMhaploid0.001", ]
paste0("n entries in treatment = ", nrow(myrest))
## [1] "n entries in treatment = 22"
paste0("n barcodes with fitness increase in treatment = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ]))
## [1] "n barcodes with fitness increase in treatment = 10"
paste0("proportion of barcodes in treatment that increased in fitness = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ])/nrow(myrest))
## [1] "proportion of barcodes in treatment that increased in fitness = 0.454545454545455"
# CM+salt diploid 1:1000
paste0("CM+salt diploid 1:1000:")
## [1] "CM+salt diploid 1:1000:"
myrest <- myres[myres$treatment == "CM+Saltdiploid0.001", ]
paste0("n entries in treatment = ", nrow(myrest))
## [1] "n entries in treatment = 22"
paste0("n barcodes with fitness increase in treatment = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ]))
## [1] "n barcodes with fitness increase in treatment = 11"
paste0("proportion of barcodes in treatment that increased in fitness = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ])/nrow(myrest))
## [1] "proportion of barcodes in treatment that increased in fitness = 0.5"
# CM+ethanol diploid 1:1000
paste0("CM+ethanol diploid 1:1000:")
## [1] "CM+ethanol diploid 1:1000:"
myrest <- myres[myres$treatment == "CM+Ethanoldiploid0.001", ]
paste0("n entries in treatment = ", nrow(myrest))
## [1] "n entries in treatment = 22"
paste0("n barcodes with fitness increase in treatment = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ]))
## [1] "n barcodes with fitness increase in treatment = 2"
paste0("proportion of barcodes in treatment that increased in fitness = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ])/nrow(myrest))
## [1] "proportion of barcodes in treatment that increased in fitness = 0.0909090909090909"
# CM diploid 1:250
paste0("CM diploid 1:250:")
## [1] "CM diploid 1:250:"
myrest <- myres[myres$treatment == "CMdiploid0.004", ]
paste0("n entries in treatment = ", nrow(myrest))
## [1] "n entries in treatment = 22"
paste0("n barcodes with fitness increase in treatment = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ]))
## [1] "n barcodes with fitness increase in treatment = 9"
paste0("proportion of barcodes in treatment that increased in fitness = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ])/nrow(myrest))
## [1] "proportion of barcodes in treatment that increased in fitness = 0.409090909090909"
# CM diploid 1:4000
paste0("CM diploid 1:4000:")
## [1] "CM diploid 1:4000:"
myrest <- myres[myres$treatment == "CMdiploid0.00025", ]
paste0("n entries in treatment = ", nrow(myrest))
## [1] "n entries in treatment = 22"
paste0("n barcodes with fitness increase in treatment = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ]))
## [1] "n barcodes with fitness increase in treatment = 0"
paste0("proportion of barcodes in treatment that increased in fitness = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ])/nrow(myrest))
## [1] "proportion of barcodes in treatment that increased in fitness = 0"
# -----------------------------------------------------------------------------
# Visualize
# ------------------------------------------------------------------- Plot
# ONLY data with se_deltafit < 0.029 for clarity -- This removes high SE
# points that have a large mean fitness increase, but have SE such that this
# fit increase is non-significant.
myx <- myres[myres$se_deltafit < 0.029, ]$m_deltafit
myr <- myres[myres$se_deltafit < 0.029, ]$m_rdeltafit
myc <- myres[myres$se_deltafit < 0.029, ]$color
mybinwidth <- 0.01
mytitle <- NULL
myylab <- "Count"
myxlab <- "Fitness change"
myhighlight <- "grey50"
myhighlight1 <- "black"
myhighlight2 <- setpalette[1]
myhighlight3 <- setpalette[2]
myfillapha <- 0.25
myptalpha <- 0.35
mylinealpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.345
mywidth <- 3.345
# p <- ggplot(myres, aes(x=myx, color = myc, fill = myc)) +
p <- ggplot(myres[myres$se_deltafit < 0.029, ], aes(x = myx, color = myc, fill = myc)) +
geom_histogram(alpha = myfillapha, position = "identity", binwidth = mybinwidth) +
geom_vline(xintercept = weighted.mean(myx, myr), linetype = "dashed", color = myhighlight) +
geom_vline(xintercept = weighted.mean(myx[myc == "fdr > 0.01"], myr[myc ==
"fdr > 0.01"]), linetype = "dashed", color = myhighlight2) + geom_vline(xintercept = weighted.mean(myx[myc ==
"fdr <= 0.01"], myr[myc == "fdr <= 0.01"]), linetype = "dashed", color = myhighlight3) +
geom_rug(alpha = myptalpha) + scale_color_manual(values = mypalette, guide = FALSE) +
scale_fill_manual(values = mypalette) + theme_classic() + theme(legend.position = c(0.8,
0.8)) + labs(fill = "Significance") + ggtitle(mytitle) + ylab(myylab) +
xlab(myxlab) + geom_hline(yintercept = 0.01, colour = "white", size = 1.01) +
theme(axis.title.y = element_text(size = mytextsize, face = "bold", margin = margin(0,
0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = mytextsize,
angle = 0)) + theme(axis.text.x = element_text(size = mytextsize, angle = 0)) +
theme(legend.title = element_text(size = mytextsize, face = "bold")) + theme(legend.text = element_text(size = mytextsize)) +
scale_x_continuous(breaks = seq(-0.1, 0.25, by = 0.05), labels = c("-0.1",
"-0.05", "0", "0.05", "0.1", "0.15", "0.2", "0.25"), limits = c(-0.05,
0.25)) + scale_y_continuous(breaks = seq(0, 25, by = 5), limits = c(0,
25))
p
## Warning: Removed 4 rows containing missing values (geom_bar).
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Figure_4.pdf", width = mywidth, height = myheight)
p
## Warning: Removed 4 rows containing missing values (geom_bar).
dev.off() # 7by7
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, mymod2, myres, myrest, p)
rm(mybinwidth, myc, myfillapha, myheight, myhighlight, myhighlight1, myhighlight2,
myhighlight3, mylinealpha, mypalette, myptalpha, myr, mytextsize, mytitle,
mywidth, myx, myxlab, myylab)Analysis: Characterization of error and contribution of covariates to SE deltafitness. 1: Build the dataframe to test SE delatfitness
my <- myfa # get a copy of the main data to work on.
# split by replicate and bind to form a wide frame.
my1 <- my[my$rep == 1, ] # r1
my2 <- my[my$rep == 2, ]
colnames(my2) <- paste0(colnames(my2), "_2") # r2
my3 <- my[my$rep == 3, ]
colnames(my3) <- paste0(colnames(my3), "_3") # r3
my4 <- my[my$rep == 4, ]
colnames(my4) <- paste0(colnames(my4), "_4") # r4
# sum(my1$bc1 != my2$bc1_2) # sort order matches.
my <- cbind(my1, my2, my3, my4) # bind
my$sedeltafit <- apply(cbind(my$deltafit, my$deltafit_2, my$deltafit_3, my$deltafit_4),
1, sjstats::se) # calculate SE deltafit among replicates
my$medcount <- apply(cbind(my$bc1cts_i_0r1, my$bc1cts_i_0r2, my$bc1cts_i_0r3,
my$bc1cts_i_0r4, my$bc1cts_f_0r1, my$bc1cts_f_0r2, my$bc1cts_f_0r3, my$bc1cts_f_0r4,
my$bc1cts_i, my$bc1cts_i_2, my$bc1cts_i_3, my$bc1cts_i_4, my$bc1cts_f, my$bc1cts_f_2,
my$bc1cts_f_3, my$bc1cts_f_4), 1, median, na.rm = T) # calculate median counts for each SE deltafit entry.
# Calculate BC proportions for next step @ all timepoints for all
# replicates.
my$prop_i_0r1 <- my$bc1cts_i_0r1/(my$bc1cts_i_0r1 + my$bc2cts_i_0r1)
my$prop_i_0r2 <- my$bc1cts_i_0r2/(my$bc1cts_i_0r2 + my$bc2cts_i_0r2)
my$prop_i_0r3 <- my$bc1cts_i_0r3/(my$bc1cts_i_0r3 + my$bc2cts_i_0r3)
my$prop_i_0r4 <- my$bc1cts_i_0r4/(my$bc1cts_i_0r4 + my$bc2cts_i_0r4)
my$prop_f_0r1 <- my$bc1cts_f_0r1/(my$bc1cts_f_0r1 + my$bc2cts_f_0r1)
my$prop_f_0r2 <- my$bc1cts_f_0r2/(my$bc1cts_f_0r2 + my$bc2cts_f_0r2)
my$prop_f_0r3 <- my$bc1cts_f_0r3/(my$bc1cts_f_0r3 + my$bc2cts_f_0r3)
my$prop_f_0r4 <- my$bc1cts_f_0r4/(my$bc1cts_f_0r4 + my$bc2cts_f_0r4)
my$prop_i_250r1 <- my$bc1cts_i/(my$bc1cts_i + my$bc2cts_i)
my$prop_i_250r2 <- my$bc1cts_i_2/(my$bc1cts_i_2 + my$bc2cts_i_2)
my$prop_i_250r3 <- my$bc1cts_i_3/(my$bc1cts_i_3 + my$bc2cts_i_3)
my$prop_i_250r4 <- my$bc1cts_i_4/(my$bc1cts_i_4 + my$bc2cts_i_4)
my$prop_f_250r1 <- my$bc1cts_f/(my$bc1cts_f + my$bc2cts_f)
my$prop_f_250r2 <- my$bc1cts_f_2/(my$bc1cts_f_2 + my$bc2cts_f_2)
my$prop_f_250r3 <- my$bc1cts_f_3/(my$bc1cts_f_3 + my$bc2cts_f_3)
my$prop_f_250r4 <- my$bc1cts_f_4/(my$bc1cts_f_4 + my$bc2cts_f_4)
my$medprop <- apply(cbind(my$prop_i_0r1, my$prop_i_0r2, my$prop_i_0r3, my$prop_i_0r4,
my$prop_f_0r1, my$prop_f_0r2, my$prop_f_0r3, my$prop_f_0r4, my$prop_i_250r1,
my$prop_i_250r2, my$prop_i_250r3, my$prop_i_250r4, my$prop_f_250r1, my$prop_f_250r2,
my$prop_f_250r3, my$prop_f_250r4), 1, median, na.rm = T) # calculate median proportion for each SE deltafit entry
my$mnfit0 <- NA # Calculate the weighted mean generation 0 fitness for each SE deltafitness entry
for (i in 1:nrow(my)) {
my$mnfit0[i] <- weighted.mean(my[i, c("fit0r1", "fit0r2", "fit0r3", "fit0r4")],
my[i, c("rfit0r1", "rfit0r2", "rfit0r3", "rfit0r4")], na.rm = T)
}
my$mnfit250 <- NA # Calculate the weighted mean generation 250 fitness for each SE deltafitness entry
for (i in 1:nrow(my)) {
my$mnfit250[i] <- weighted.mean(my[i, c("fit25", "fit25_2", "fit25_3", "fit25_4")],
my[i, c("rfit25", "rfit25_2", "rfit25_3", "rfit25_4")], na.rm = T)
}
# NOT USED IN MODEL-- captures same data as fit0 + fit250
my$mndeltafit <- NA
for (i in 1:nrow(my)) {
my$mndeltafit[i] <- weighted.mean(my[i, c("deltafit", "deltafit_2", "deltafit_3",
"deltafit_4")], my[i, c("rdeltafit", "rdeltafit_2", "rdeltafit_3", "rdeltafit_4")],
na.rm = T)
}
my$mnccdeltafit <- NA # Calculate the weighted mean cross contamination rate for each SE deltafitness entry
for (i in 1:nrow(my)) {
my$mnccdeltafit[i] <- weighted.mean(my[i, c("ccdeltafit", "ccdeltafit_2",
"ccdeltafit_3", "ccdeltafit_4")], my[i, c("rdeltafit", "rdeltafit_2",
"rdeltafit_3", "rdeltafit_4")], na.rm = T)
}
my$mnrdeltafit <- apply(cbind(my$rdeltafit, my$rdeltafit_2, my$rdeltafit_3,
my$rdeltafit_4), 1, mean, na.rm = T) # calculate an updated 'sampling effort' value for each SE deltafitness entry
myfa_w <- my
rm(my, my1, my2, my3, my4, i)2: Assess variables that affect SE deltafitness: using sequential term removal via p-value
mymod <- lm(I(scale(myfa_w$sedeltafit)) ~ I(scale(myfa_w$mnccdeltafit)) + I(scale(myfa_w$mndeltafit)) +
I(scale(myfa_w$medcount)) + I(scale(myfa_w$medprop)) + myfa_w$treatment,
weights = myfa_w$mnrdeltafit)
car::vif(mymod) # VIF looks okay.
## GVIF Df GVIF^(1/(2*Df))
## I(scale(myfa_w$mnccdeltafit)) 1.819921 1 1.349045
## I(scale(myfa_w$mndeltafit)) 1.503512 1 1.226178
## I(scale(myfa_w$medcount)) 2.264308 1 1.504762
## I(scale(myfa_w$medprop)) 1.878926 1 1.370739
## myfa_w$treatment 3.261989 5 1.125507
summary(mymod)
##
## Call:
## lm(formula = I(scale(myfa_w$sedeltafit)) ~ I(scale(myfa_w$mnccdeltafit)) +
## I(scale(myfa_w$mndeltafit)) + I(scale(myfa_w$medcount)) +
## I(scale(myfa_w$medprop)) + myfa_w$treatment, weights = myfa_w$mnrdeltafit)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -66.450 -18.399 -3.423 13.187 125.556
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.10948 0.08372 -1.308
## I(scale(myfa_w$mnccdeltafit)) 0.13240 0.05085 2.604
## I(scale(myfa_w$mndeltafit)) 0.31846 0.09076 3.509
## I(scale(myfa_w$medcount)) 0.04030 0.05250 0.768
## I(scale(myfa_w$medprop)) -0.10176 0.06031 -1.687
## myfa_w$treatmentCM+Ethanoldiploid0.001 0.14585 0.13639 1.069
## myfa_w$treatmentCM+Saltdiploid0.001 -0.08633 0.16839 -0.513
## myfa_w$treatmentCMdiploid0.00025 -0.02988 0.12585 -0.237
## myfa_w$treatmentCMdiploid0.004 -0.10866 0.14473 -0.751
## myfa_w$treatmentCMhaploid0.001 -0.06310 0.14083 -0.448
## Pr(>|t|)
## (Intercept) 0.193073
## I(scale(myfa_w$mnccdeltafit)) 0.010201 *
## I(scale(myfa_w$mndeltafit)) 0.000603 ***
## I(scale(myfa_w$medcount)) 0.444014
## I(scale(myfa_w$medprop)) 0.093743 .
## myfa_w$treatmentCM+Ethanoldiploid0.001 0.286752
## myfa_w$treatmentCM+Saltdiploid0.001 0.608977
## myfa_w$treatmentCMdiploid0.00025 0.812696
## myfa_w$treatmentCMdiploid0.004 0.454045
## myfa_w$treatmentCMhaploid0.001 0.654807
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30.44 on 142 degrees of freedom
## Multiple R-squared: 0.1379, Adjusted R-squared: 0.08322
## F-statistic: 2.523 on 9 and 142 DF, p-value: 0.01033
# model output
# -----------------------------------------------------------------=
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
show.r2 = F, file = "000_Additional_File_11.html") # r2 on the lm version of htis model is |  | I(scale(myfa w\(sedeltafit))</th> </tr> <tr> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; text-align:left; ">Predictors</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">Estimates</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">CI</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">Statistic</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">p</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">(Intercept)</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.10948</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.27356 – 0.05460</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-1.30774</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.19307337</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">I(scale(myfa_w\)mnccdeltafit)) | 0.13240 | 0.03274 – 0.23207 | 2.60374 | 0.01020124 | |||
|---|---|---|---|---|---|---|---|---|
| I(scale(myfa_w\(mndeltafit))</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.31846</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.14057 – 0.49635</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">3.50878</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; "><strong>0.00060335</strong></td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">I(scale(myfa_w\)medcount)) | 0.04030 | -0.06260 – 0.14320 | 0.76757 | 0.44401445 | ||||
| I(scale(myfa_w$medprop)) | -0.10176 | -0.21997 – 0.01644 | -1.68729 | 0.09374310 | ||||
| CM+Ethanoldiploid 0.001 | 0.14585 | -0.12148 – 0.41317 | 1.06929 | 0.28675155 | ||||
| CM+Saltdiploid 0.001 | -0.08633 | -0.41636 – 0.24371 | -0.51267 | 0.60897704 | ||||
| C Mdiploid 0.00025 | -0.02988 | -0.27653 – 0.21678 | -0.23739 | 0.81269600 | ||||
| C Mdiploid 0.004 | -0.10866 | -0.39232 – 0.17501 | -0.75075 | 0.45404536 | ||||
| C Mhaploid 0.001 | -0.06310 | -0.33913 – 0.21293 | -0.44804 | 0.65480662 | ||||
| Observations | 152 | |||||||
# -----------------------------------------------------------------------------
rm(mymod)Analysis: Power (Look at the numbers)
# POC FIT ASSAY -- GET RMSE & EFFECTSIZE
# ---------------------------------------
my <- poc92[order(poc92$bcid), ] # get data
my$rw_corrected <- my$rw - weighted.mean(my$rw, my$r) # calculate mean corrected init fitness
mymod <- lm(my$rw_corrected ~ my$bcid + 0, weights = my$r)
summary(mymod) # model for rmse in POC assays
##
## Call:
## lm(formula = my$rw_corrected ~ my$bcid + 0, weights = my$r)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -4.7350 -0.5858 -0.0109 0.5304 5.3405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## my$bcidd1A10 0.00132555 0.00371437 0.357 0.721280
## my$bcidd1A11 -0.00762370 0.00466055 -1.636 0.102267
## my$bcidd1A2 0.00811450 0.00396019 2.049 0.040778 *
## my$bcidd1A3 0.00494242 0.00439553 1.124 0.261165
## my$bcidd1A4 0.00795573 0.00448555 1.774 0.076495 .
## my$bcidd1A5 0.00103320 0.00520070 0.199 0.842573
## my$bcidd1A6 -0.00085773 0.00357809 -0.240 0.810610
## my$bcidd1A7 0.00344369 0.00512097 0.672 0.501476
## my$bcidd1A8 0.00583557 0.00414724 1.407 0.159778
## my$bcidd1A9 0.00724382 0.00374750 1.933 0.053584 .
## my$bcidd1B1 -0.00527477 0.00502129 -1.050 0.293807
## my$bcidd1B10 -0.00821202 0.00615734 -1.334 0.182674
## my$bcidd1B11 -0.01330702 0.00511428 -2.602 0.009437 **
## my$bcidd1B12 -0.00741678 0.00553232 -1.341 0.180413
## my$bcidd1B2 0.00289719 0.00431315 0.672 0.501957
## my$bcidd1B3 0.00354456 0.00434513 0.816 0.414878
## my$bcidd1B4 0.00951091 0.00421461 2.257 0.024293 *
## my$bcidd1B5 0.00961576 0.00421606 2.281 0.022819 *
## my$bcidd1B6 0.00134667 0.00501745 0.268 0.788462
## my$bcidd1B7 -0.00398474 0.00435334 -0.915 0.360287
## my$bcidd1B8 0.00386754 0.00435584 0.888 0.374856
## my$bcidd1B9 -0.00197406 0.00517408 -0.382 0.702909
## my$bcidd1C1 -0.00130215 0.00523917 -0.249 0.803778
## my$bcidd1C10 -0.00200878 0.00612990 -0.328 0.743221
## my$bcidd1C11 0.00332553 0.00513862 0.647 0.517707
## my$bcidd1C12 -0.00495638 0.00525931 -0.942 0.346266
## my$bcidd1C2 0.00310014 0.00440966 0.703 0.482235
## my$bcidd1C3 0.00170870 0.00544151 0.314 0.753591
## my$bcidd1C4 0.00064326 0.00445091 0.145 0.885124
## my$bcidd1C5 -0.00293377 0.00470909 -0.623 0.533457
## my$bcidd1C6 -0.00108375 0.00401081 -0.270 0.787069
## my$bcidd1C7 -0.00224296 0.00857907 -0.261 0.793814
## my$bcidd1C8 -0.00815331 0.00535937 -1.521 0.128566
## my$bcidd1C9 0.01983075 0.00598788 3.312 0.000968 ***
## my$bcidd1D1 0.00371456 0.00415556 0.894 0.371651
## my$bcidd1D10 0.01036458 0.00405675 2.555 0.010802 *
## my$bcidd1D11 -0.00004981 0.00380942 -0.013 0.989570
## my$bcidd1D12 0.00301900 0.00386910 0.780 0.435449
## my$bcidd1D2 -0.01098378 0.02236414 -0.491 0.623464
## my$bcidd1D3 -0.00440055 0.00562205 -0.783 0.434011
## my$bcidd1D4 -0.00693858 0.00737022 -0.941 0.346760
## my$bcidd1D5 -0.00326940 0.00563709 -0.580 0.562087
## my$bcidd1D6 -0.00844038 0.00554061 -1.523 0.128053
## my$bcidd1D7 -0.00168313 0.00462568 -0.364 0.716052
## my$bcidd1D8 0.00180750 0.00397535 0.455 0.649462
## my$bcidd1D9 0.00014080 0.00545635 0.026 0.979419
## my$bcidd1E1 -0.01270595 0.00523534 -2.427 0.015441 *
## my$bcidd1E10 -0.00147548 0.00433537 -0.340 0.733691
## my$bcidd1E11 -0.00708010 0.00406068 -1.744 0.081609 .
## my$bcidd1E12 0.01157108 0.00442433 2.615 0.009078 **
## my$bcidd1E2 0.00500682 0.00373187 1.342 0.180085
## my$bcidd1E3 0.00762603 0.00554298 1.376 0.169259
## my$bcidd1E4 0.00591987 0.00384425 1.540 0.123964
## my$bcidd1E5 -0.00657763 0.00498803 -1.319 0.187644
## my$bcidd1E6 -0.00139332 0.00372470 -0.374 0.708444
## my$bcidd1E7 0.00852322 0.00396829 2.148 0.032020 *
## my$bcidd1E8 -0.00369015 0.00443221 -0.833 0.405327
## my$bcidd1E9 0.00639603 0.00436339 1.466 0.143076
## my$bcidd1F1 -0.00012498 0.00381935 -0.033 0.973904
## my$bcidd1F10 0.00393912 0.00423405 0.930 0.352467
## my$bcidd1F11 0.00670256 0.00469224 1.428 0.153547
## my$bcidd1F12 0.00778245 0.00385357 2.020 0.043756 *
## my$bcidd1F2 0.00582870 0.00435141 1.339 0.180780
## my$bcidd1F3 0.00651265 0.00596554 1.092 0.275281
## my$bcidd1F4 -0.02625366 0.00923796 -2.842 0.004596 **
## my$bcidd1F5 -0.04834088 0.00429859 -11.246 < 0.0000000000000002 ***
## my$bcidd1F6 -0.00012930 0.00381509 -0.034 0.972972
## my$bcidd1F7 -0.01483489 0.00492987 -3.009 0.002700 **
## my$bcidd1F8 -0.00220117 0.00605837 -0.363 0.716455
## my$bcidd1F9 0.00029767 0.01166664 0.026 0.979651
## my$bcidd1G1 0.01006985 0.00654635 1.538 0.124376
## my$bcidd1G10 0.00690433 0.00358418 1.926 0.054408 .
## my$bcidd1G11 0.00299309 0.00557059 0.537 0.591205
## my$bcidd1G12 0.00147423 0.00344561 0.428 0.668867
## my$bcidd1G2 0.00615615 0.00569582 1.081 0.280096
## my$bcidd1G3 0.00097634 0.00390830 0.250 0.802795
## my$bcidd1G4 -0.00335673 0.00478773 -0.701 0.483433
## my$bcidd1G5 -0.01510445 0.00537164 -2.812 0.005043 **
## my$bcidd1G6 -0.00650477 0.00473025 -1.375 0.169463
## my$bcidd1G7 -0.00911754 0.00430618 -2.117 0.034534 *
## my$bcidd1G8 -0.00467010 0.00489124 -0.955 0.339967
## my$bcidd1G9 -0.00345997 0.00470652 -0.735 0.462463
## my$bcidd1H11 0.00188567 0.00387909 0.486 0.627018
## my$bcidd1H2 -0.00688077 0.00535513 -1.285 0.199193
## my$bcidd1H3 -0.00074760 0.00640698 -0.117 0.907138
## my$bcidd1H4 -0.03016766 0.00925002 -3.261 0.001155 **
## my$bcidd1H5 -0.00289320 0.00438300 -0.660 0.509377
## my$bcidd1H6 0.00117090 0.00365259 0.321 0.748621
## my$bcidd1H7 -0.00459377 0.00852914 -0.539 0.590312
## my$bcidd1H8 -0.00449698 0.00614172 -0.732 0.464255
## my$bcidd1H9 -0.00798745 0.00584794 -1.366 0.172359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.127 on 819 degrees of freedom
## Multiple R-squared: 0.2653, Adjusted R-squared: 0.1836
## F-statistic: 3.25 on 91 and 819 DF, p-value: < 0.00000000000000022
myrmse <- sqrt(mean(residuals(mymod)^2)) * 100 # this is the rmse from the fitness equivalence model*100 --> effect size of 1 corresonds to an ~1.76 fitness change
myeffectsize <- 1/myrmse # effect size of 1 is a 1.76 fitnesss change, so for a fitness change of 1.00, expect effect size of 1/myrmse
# ------------------------------------------------------------------------------
# POC FIT ASSAY -- CACULATE ESTIMATED POWER TO DETECT TREAT EFF IN NEXT EXP
# ---- power to detect treat effects
pow <- pwr.f2.test(u = 1, v = ((22 * 2) - 1 - 1), f2 = myeffectsize, sig.level = 0.05)
pow$power # 99.8% power to detect treatment fitness differences of 1.00%
## [1] 0.9982838
# ------------------------------------------------------------------------------
# POC FIT ASSAY -- CALCULATE POWER TO DETECT FITNESS DIFFERENCE FOR INDIV
# BCS --
my <- poc92
my1 <- my[my$exp == 1, ]
colnames(my1) <- paste0(colnames(my1), "_R1")
my2 <- my[my$exp == 2, ]
colnames(my2) <- paste0(colnames(my2), "_R2")
my3 <- my[my$exp == 3, ]
colnames(my3) <- paste0(colnames(my3), "_R3")
my4 <- my[my$exp == 4, ]
colnames(my4) <- paste0(colnames(my4), "_R4")
my5 <- my[my$exp == 5, ]
colnames(my5) <- paste0(colnames(my5), "_R5")
my6 <- my[my$exp == 6, ]
colnames(my6) <- paste0(colnames(my6), "_R6")
my7 <- my[my$exp == 7, ]
colnames(my7) <- paste0(colnames(my7), "_R7")
my8 <- my[my$exp == 8, ]
colnames(my8) <- paste0(colnames(my8), "_R8")
my9 <- my[my$exp == 9, ]
colnames(my9) <- paste0(colnames(my9), "_R9")
my10 <- my[my$exp == 10, ]
colnames(my10) <- paste0(colnames(my10), "_R10")
poc92_w <- cbind(my1, my2, my3, my4, my5, my6, my7, my8, my9, my10)
for (i in 1:nrow(poc92_w)) {
poc92_w$mnrw[i] <- weighted.mean(poc92_w[i, c("rw_R1", "rw_R2", "rw_R3",
"rw_R4", "rw_R5", "rw_R6", "rw_R7", "rw_R8", "rw_R9", "rw_R10")], poc92_w[i,
c("r_R1", "r_R2", "r_R3", "r_R4", "r_R5", "r_R6", "r_R7", "r_R8", "r_R9",
"r_R10")], na.rm = T)
poc92_w$mnr[i] <- rowMeans(poc92_w[i, c("r_R1", "r_R2", "r_R3", "r_R4",
"r_R5", "r_R6", "r_R7", "r_R8", "r_R9", "r_R10")], na.rm = T)
}
my <- poc92_w
my$psd_r1 <- ((my$rw_R1 - my$mnrw)^2)/10
my$psd_r2 <- ((my$rw_R2 - my$mnrw)^2)/10
my$psd_r3 <- ((my$rw_R3 - my$mnrw)^2)/10
my$psd_r4 <- ((my$rw_R4 - my$mnrw)^2)/10
my$psd_r5 <- ((my$rw_R5 - my$mnrw)^2)/10
my$psd_r6 <- ((my$rw_R6 - my$mnrw)^2)/10
my$psd_r7 <- ((my$rw_R7 - my$mnrw)^2)/10
my$psd_r8 <- ((my$rw_R8 - my$mnrw)^2)/10
my$psd_r9 <- ((my$rw_R9 - my$mnrw)^2)/10
my$psd_r10 <- ((my$rw_R10 - my$mnrw)^2)/10
my$psd_m <- rowMeans(cbind(my$psd_r1, my$psd_r2, my$psd_r3, my$psd_r4, my$psd_r5,
my$psd_r6, my$psd_r7, my$psd_r8, my$psd_r9, my$psd_r10), na.rm = T)
my$psd <- sqrt(my$psd_m)
psd <- weighted.mean(my$psd, my$mnr, na.rm = T)
pow <- pwr.t.test(d = 0.01/psd, n = 4, sig.level = 0.05)
pow$power # 67% power to detect 1% fit change.
## [1] 0.6669863
pow <- pwr.t.test(power = 0.8, n = 4, sig.level = 0.05)
pow$d * psd # 80% power to detect ~1.2% fitness difference
## [1] 0.01176284
# ------------------------------------------------------------------------------
# 250 GEN EXP -- GET RMSE & EFFECTSIZE
# -----------------------------------------
my <- myfa[order(myfa$bctID), ]
mymod <- lm(my$deltafit ~ my$bctID + 0, weights = my$rdeltafit)
summary(mymod)
##
## Call:
## lm(formula = my$deltafit ~ my$bctID + 0, weights = my$rdeltafit)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -8.9024 -0.9879 0.0734 0.9561 4.8229
##
## Coefficients:
## Estimate Std. Error t value
## my$bctIDCM+Ethanoldiploid0.001d1A2 0.00224666 0.01223756 0.184
## my$bctIDCM+Ethanoldiploid0.001d1A4 0.01765500 0.01365937 1.293
## my$bctIDCM+Ethanoldiploid0.001d1A5 -0.00183138 0.01601185 -0.114
## my$bctIDCM+Ethanoldiploid0.001d1A6 0.00847721 0.01160185 0.731
## my$bctIDCM+Ethanoldiploid0.001d1A7 0.00837054 0.01577339 0.531
## my$bctIDCM+Ethanoldiploid0.001d1D10 0.03354979 0.01054805 3.181
## my$bctIDCM+Ethanoldiploid0.001d1D11 0.02980938 0.01093362 2.726
## my$bctIDCM+Ethanoldiploid0.001d1D12 0.02399057 0.01018367 2.356
## my$bctIDCM+Ethanoldiploid0.001d1D9 0.02343344 0.02027118 1.156
## my$bctIDCM+Ethanoldiploid0.001d1E10 0.04807661 0.01120179 4.292
## my$bctIDCM+Ethanoldiploid0.001d1E11 0.03162314 0.01125755 2.809
## my$bctIDCM+Ethanoldiploid0.001d1E12 -0.03049231 0.01211023 -2.518
## my$bctIDCM+Ethanoldiploid0.001d1E9 -0.00999714 0.01070592 -0.934
## my$bctIDCM+Ethanoldiploid0.001d1F1 0.00938513 0.00879011 1.068
## my$bctIDCM+Ethanoldiploid0.001d1F2 0.01938389 0.01121524 1.728
## my$bctIDCM+Ethanoldiploid0.001d1G1 0.01657615 0.01500470 1.105
## my$bctIDCM+Ethanoldiploid0.001d1G2 -0.01127658 0.01818702 -0.620
## my$bctIDCM+Ethanoldiploid0.001d1H2 -0.01386016 0.01837215 -0.754
## my$bctIDCM+Ethanoldiploid0.001d1H4 0.00296372 0.02376768 0.125
## my$bctIDCM+Ethanoldiploid0.001d1H5 0.00347374 0.01381229 0.251
## my$bctIDCM+Ethanoldiploid0.001d1H6 0.00333766 0.01279047 0.261
## my$bctIDCM+Ethanoldiploid0.001d1H7 -0.00551949 0.02558074 -0.216
## my$bctIDCM+Saltdiploid0.001d1A2 0.04403600 0.01205900 3.652
## my$bctIDCM+Saltdiploid0.001d1A3 0.01686781 0.01180626 1.429
## my$bctIDCM+Saltdiploid0.001d1A4 0.02820342 0.01240104 2.274
## my$bctIDCM+Saltdiploid0.001d1A5 0.39852910 0.19667177 2.026
## my$bctIDCM+Saltdiploid0.001d1A7 0.02578701 0.01322947 1.949
## my$bctIDCM+Saltdiploid0.001d1F3 0.14079891 0.02033980 6.922
## my$bctIDCM+Saltdiploid0.001d1F4 0.18806044 0.02296431 8.189
## my$bctIDCM+Saltdiploid0.001d1F5 0.19913858 0.15799739 1.260
## my$bctIDCM+Saltdiploid0.001d1F6 0.23451078 0.01480773 15.837
## my$bctIDCM+Saltdiploid0.001d1F7 0.15255139 0.02121079 7.192
## my$bctIDCM+Saltdiploid0.001d1F8 0.12888704 0.01760927 7.319
## my$bctIDCM+Saltdiploid0.001d1G3 0.10101471 0.03918185 2.578
## my$bctIDCM+Saltdiploid0.001d1G4 0.13402619 0.04517263 2.967
## my$bctIDCM+Saltdiploid0.001d1G5 0.09585476 0.01936410 4.950
## my$bctIDCM+Saltdiploid0.001d1G6 0.08971402 0.03285475 2.731
## my$bctIDCM+Saltdiploid0.001d1G7 0.08740043 0.02106254 4.150
## my$bctIDCM+Saltdiploid0.001d1G8 0.12572385 0.03290001 3.821
## my$bctIDCM+Saltdiploid0.001d1H2 0.00495816 0.01539561 0.322
## my$bctIDCM+Saltdiploid0.001d1H3 0.02115633 0.01617586 1.308
## my$bctIDCM+Saltdiploid0.001d1H4 0.02137396 0.01932366 1.106
## my$bctIDCM+Saltdiploid0.001d1H5 0.00354951 0.01200179 0.296
## my$bctIDCM+Saltdiploid0.001d1H7 0.09417670 0.02624046 3.589
## my$bctIDCMdiploid0.00025d1A2 0.01559982 0.01599335 0.975
## my$bctIDCMdiploid0.00025d1A3 -0.00878718 0.01495776 -0.587
## my$bctIDCMdiploid0.00025d1A4 0.00174608 0.01441827 0.121
## my$bctIDCMdiploid0.00025d1A5 0.02677688 0.01511949 1.771
## my$bctIDCMdiploid0.00025d1A6 0.01198957 0.01303287 0.920
## my$bctIDCMdiploid0.00025d1A7 -0.00440271 0.01621492 -0.272
## my$bctIDCMdiploid0.00025d1B10 -0.00809898 0.01653299 -0.490
## my$bctIDCMdiploid0.00025d1B11 0.01723177 0.01242047 1.387
## my$bctIDCMdiploid0.00025d1B12 0.02983743 0.01830367 1.630
## my$bctIDCMdiploid0.00025d1B9 0.02155039 0.01336058 1.613
## my$bctIDCMdiploid0.00025d1C10 -0.01674094 0.01859239 -0.900
## my$bctIDCMdiploid0.00025d1C11 0.03216629 0.01622343 1.983
## my$bctIDCMdiploid0.00025d1C12 0.01496869 0.01326625 1.128
## my$bctIDCMdiploid0.00025d1C9 -0.00518047 0.01188006 -0.436
## my$bctIDCMdiploid0.00025d1D2 0.00370897 0.05550949 0.067
## my$bctIDCMdiploid0.00025d1E2 0.01289359 0.01052145 1.225
## my$bctIDCMdiploid0.00025d1H2 0.00760594 0.01762321 0.432
## my$bctIDCMdiploid0.00025d1H3 -0.00458899 0.01745253 -0.263
## my$bctIDCMdiploid0.00025d1H4 0.01843903 0.02426240 0.760
## my$bctIDCMdiploid0.00025d1H5 0.02541993 0.01377634 1.845
## my$bctIDCMdiploid0.00025d1H6 0.00414724 0.01352400 0.307
## my$bctIDCMdiploid0.00025d1H7 0.00190764 0.02782539 0.069
## my$bctIDCMdiploid0.001d1A11 0.03612225 0.01815435 1.990
## my$bctIDCMdiploid0.001d1A2 0.00809289 0.01417709 0.571
## my$bctIDCMdiploid0.001d1A2B 0.00927271 0.01214216 0.764
## my$bctIDCMdiploid0.001d1A3 0.00032174 0.01485237 0.022
## my$bctIDCMdiploid0.001d1A3B 0.00398055 0.01385717 0.287
## my$bctIDCMdiploid0.001d1A4 0.01780845 0.01310805 1.359
## my$bctIDCMdiploid0.001d1A4B 0.00153330 0.01338588 0.115
## my$bctIDCMdiploid0.001d1A5 0.02937872 0.01569201 1.872
## my$bctIDCMdiploid0.001d1A5B -0.00512256 0.01465871 -0.349
## my$bctIDCMdiploid0.001d1A6 0.02327336 0.01302785 1.786
## my$bctIDCMdiploid0.001d1A6B 0.00955064 0.01339667 0.713
## my$bctIDCMdiploid0.001d1A7 0.04290593 0.01344789 3.191
## my$bctIDCMdiploid0.001d1A8 0.01512575 0.01169949 1.293
## my$bctIDCMdiploid0.001d1A9 0.02268337 0.01128163 2.011
## my$bctIDCMdiploid0.001d1B1 0.05232713 0.01916650 2.730
## my$bctIDCMdiploid0.001d1B2 0.04063255 0.01363624 2.980
## my$bctIDCMdiploid0.001d1C1 0.05496896 0.01813523 3.031
## my$bctIDCMdiploid0.001d1C2 0.03217244 0.01299148 2.476
## my$bctIDCMdiploid0.001d1D4 0.08813997 0.05292837 1.665
## my$bctIDCMdiploid0.001d1D5 -0.01879019 0.01310839 -1.433
## my$bctIDCMdiploid0.001d1D6 0.04266468 0.04243456 1.005
## my$bctIDCMdiploid0.001d1D7 0.01221091 0.01202676 1.015
## my$bctIDCMdiploid0.001d1D8 -0.01270792 0.01081313 -1.175
## my$bctIDCMdiploid0.001d1E4 0.00301728 0.00782870 0.385
## my$bctIDCMdiploid0.001d1E5 0.03661495 0.02836404 1.291
## my$bctIDCMdiploid0.001d1E6 -0.02094510 0.00919326 -2.278
## my$bctIDCMdiploid0.001d1E7 0.02727065 0.01036588 2.631
## my$bctIDCMdiploid0.001d1E8 -0.00435115 0.01093102 -0.398
## my$bctIDCMdiploid0.001d1H11 0.03103102 0.01491899 2.080
## my$bctIDCMdiploid0.001d1H2 0.01207171 0.01797336 0.672
## my$bctIDCMdiploid0.001d1H2B -0.01771525 0.01722331 -1.029
## my$bctIDCMdiploid0.001d1H3 -0.00572467 0.01932551 -0.296
## my$bctIDCMdiploid0.001d1H3B 0.00004411 0.01824231 0.002
## my$bctIDCMdiploid0.001d1H4 0.00887392 0.02490203 0.356
## my$bctIDCMdiploid0.001d1H4B -0.00268735 0.02541084 -0.106
## my$bctIDCMdiploid0.001d1H5 0.01359003 0.01528546 0.889
## my$bctIDCMdiploid0.001d1H5B -0.00274906 0.01359686 -0.202
## my$bctIDCMdiploid0.001d1H6 0.03458717 0.01367055 2.530
## my$bctIDCMdiploid0.001d1H6B 0.02557412 0.01329414 1.924
## my$bctIDCMdiploid0.001d1H7 0.00942620 0.02647801 0.356
## my$bctIDCMdiploid0.001d1H8 0.01372130 0.02208970 0.621
## my$bctIDCMdiploid0.001d1H9 0.01922550 0.02317816 0.829
## my$bctIDCMdiploid0.004d1A2 0.00073845 0.01697648 0.043
## my$bctIDCMdiploid0.004d1A3 0.01219203 0.01903325 0.641
## my$bctIDCMdiploid0.004d1A4 0.05056340 0.01731766 2.920
## my$bctIDCMdiploid0.004d1A5 0.05149485 0.01798080 2.864
## my$bctIDCMdiploid0.004d1A6 0.01931859 0.01722893 1.121
## my$bctIDCMdiploid0.004d1A7 0.02865780 0.02004130 1.430
## my$bctIDCMdiploid0.004d1B3 0.02462782 0.01445493 1.704
## my$bctIDCMdiploid0.004d1B4 0.03125331 0.01659619 1.883
## my$bctIDCMdiploid0.004d1B5 0.04298949 0.01367318 3.144
## my$bctIDCMdiploid0.004d1B6 0.05327518 0.01738231 3.065
## my$bctIDCMdiploid0.004d1B7 0.07749931 0.01526527 5.077
## my$bctIDCMdiploid0.004d1C3 0.04273794 0.01469507 2.908
## my$bctIDCMdiploid0.004d1C4 0.02312320 0.01388803 1.665
## my$bctIDCMdiploid0.004d1C5 0.04334705 0.01293091 3.352
## my$bctIDCMdiploid0.004d1C6 0.06362223 0.01397915 4.551
## my$bctIDCMdiploid0.004d1C7 0.03835768 0.02896918 1.324
## my$bctIDCMdiploid0.004d1H2 0.02225147 0.02308198 0.964
## my$bctIDCMdiploid0.004d1H3 0.02106572 0.02173722 0.969
## my$bctIDCMdiploid0.004d1H4 0.00548235 0.03044485 0.180
## my$bctIDCMdiploid0.004d1H5 0.06026645 0.01911119 3.153
## my$bctIDCMdiploid0.004d1H6 0.04022208 0.01766259 2.277
## my$bctIDCMdiploid0.004d1H7 0.01164662 0.03155608 0.369
## my$bctIDCMhaploid0.001d1A2 0.17748276 0.18501465 0.959
## my$bctIDCMhaploid0.001d1A3 0.03475922 0.01642162 2.117
## my$bctIDCMhaploid0.001d1A3B 0.02653209 0.01137336 2.333
## my$bctIDCMhaploid0.001d1A4 0.07888998 0.01157424 6.816
## my$bctIDCMhaploid0.001d1A4B 0.00800659 0.01199416 0.668
## my$bctIDCMhaploid0.001d1A5 0.07567154 0.01371413 5.518
## my$bctIDCMhaploid0.001d1A5B 0.05376713 0.01259551 4.269
## my$bctIDCMhaploid0.001d1A6 0.02913551 0.01052784 2.767
## my$bctIDCMhaploid0.001d1A6B 0.02748147 0.00908708 3.024
## my$bctIDCMhaploid0.001d1A7 0.28424691 0.22971593 1.237
## my$bctIDCMhaploid0.001d1A7B 0.37549211 0.27832375 1.349
## my$bctIDCMhaploid0.001d1H2 0.05734718 0.01492184 3.843
## my$bctIDCMhaploid0.001d1H3 0.00588798 0.01683299 0.350
## my$bctIDCMhaploid0.001d1H3B 0.02271945 0.01846922 1.230
## my$bctIDCMhaploid0.001d1H4 0.06491278 0.03830520 1.695
## my$bctIDCMhaploid0.001d1H4B 0.01454690 0.01990335 0.731
## my$bctIDCMhaploid0.001d1H5 0.24925362 0.21028584 1.185
## my$bctIDCMhaploid0.001d1H5B 0.07768822 0.01659155 4.682
## my$bctIDCMhaploid0.001d1H6 0.10010351 0.02954796 3.388
## my$bctIDCMhaploid0.001d1H6B 0.11256605 0.02312772 4.867
## my$bctIDCMhaploid0.001d1H7 0.08865934 0.02084663 4.253
## my$bctIDCMhaploid0.001d1H7B 0.07939256 0.01932983 4.107
## Pr(>|t|)
## my$bctIDCM+Ethanoldiploid0.001d1A2 0.854419
## my$bctIDCM+Ethanoldiploid0.001d1A4 0.196832
## my$bctIDCM+Ethanoldiploid0.001d1A5 0.908990
## my$bctIDCM+Ethanoldiploid0.001d1A6 0.465352
## my$bctIDCM+Ethanoldiploid0.001d1A7 0.595903
## my$bctIDCM+Ethanoldiploid0.001d1D10 0.001570 **
## my$bctIDCM+Ethanoldiploid0.001d1D11 0.006650 **
## my$bctIDCM+Ethanoldiploid0.001d1D12 0.018906 *
## my$bctIDCM+Ethanoldiploid0.001d1D9 0.248287
## my$bctIDCM+Ethanoldiploid0.001d1E10 0.00002165288566661 ***
## my$bctIDCM+Ethanoldiploid0.001d1E11 0.005182 **
## my$bctIDCM+Ethanoldiploid0.001d1E12 0.012147 *
## my$bctIDCM+Ethanoldiploid0.001d1E9 0.350903
## my$bctIDCM+Ethanoldiploid0.001d1F1 0.286224
## my$bctIDCM+Ethanoldiploid0.001d1F2 0.084602 .
## my$bctIDCM+Ethanoldiploid0.001d1G1 0.269859
## my$bctIDCM+Ethanoldiploid0.001d1G2 0.535545
## my$bctIDCM+Ethanoldiploid0.001d1H2 0.450992
## my$bctIDCM+Ethanoldiploid0.001d1H4 0.900820
## my$bctIDCM+Ethanoldiploid0.001d1H5 0.801543
## my$bctIDCM+Ethanoldiploid0.001d1H6 0.794250
## my$bctIDCM+Ethanoldiploid0.001d1H7 0.829265
## my$bctIDCM+Saltdiploid0.001d1A2 0.000291 ***
## my$bctIDCM+Saltdiploid0.001d1A3 0.153770
## my$bctIDCM+Saltdiploid0.001d1A4 0.023413 *
## my$bctIDCM+Saltdiploid0.001d1A5 0.043309 *
## my$bctIDCM+Saltdiploid0.001d1A7 0.051883 .
## my$bctIDCM+Saltdiploid0.001d1F3 0.00000000001514729 ***
## my$bctIDCM+Saltdiploid0.001d1F4 0.00000000000000266 ***
## my$bctIDCM+Saltdiploid0.001d1F5 0.208173
## my$bctIDCM+Saltdiploid0.001d1F6 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F7 0.00000000000263293 ***
## my$bctIDCM+Saltdiploid0.001d1F8 0.00000000000113446 ***
## my$bctIDCM+Saltdiploid0.001d1G3 0.010247 *
## my$bctIDCM+Saltdiploid0.001d1G4 0.003166 **
## my$bctIDCM+Saltdiploid0.001d1G5 0.00000104573824082 ***
## my$bctIDCM+Saltdiploid0.001d1G6 0.006566 **
## my$bctIDCM+Saltdiploid0.001d1G7 0.00003974952106726 ***
## my$bctIDCM+Saltdiploid0.001d1G8 0.000151 ***
## my$bctIDCM+Saltdiploid0.001d1H2 0.747562
## my$bctIDCM+Saltdiploid0.001d1H3 0.191568
## my$bctIDCM+Saltdiploid0.001d1H4 0.269265
## my$bctIDCM+Saltdiploid0.001d1H5 0.767557
## my$bctIDCM+Saltdiploid0.001d1H7 0.000368 ***
## my$bctIDCMdiploid0.00025d1A2 0.329882
## my$bctIDCMdiploid0.00025d1A3 0.557181
## my$bctIDCMdiploid0.00025d1A4 0.903664
## my$bctIDCMdiploid0.00025d1A5 0.077226 .
## my$bctIDCMdiploid0.00025d1A6 0.358086
## my$bctIDCMdiploid0.00025d1A7 0.786112
## my$bctIDCMdiploid0.00025d1B10 0.624463
## my$bctIDCMdiploid0.00025d1B11 0.166007
## my$bctIDCMdiploid0.00025d1B12 0.103764
## my$bctIDCMdiploid0.00025d1B9 0.107440
## my$bctIDCMdiploid0.00025d1C10 0.368373
## my$bctIDCMdiploid0.00025d1C11 0.048000 *
## my$bctIDCMdiploid0.00025d1C12 0.259774
## my$bctIDCMdiploid0.00025d1C9 0.662997
## my$bctIDCMdiploid0.00025d1D2 0.946757
## my$bctIDCMdiploid0.00025d1E2 0.221035
## my$bctIDCMdiploid0.00025d1H2 0.666246
## my$bctIDCMdiploid0.00025d1H3 0.792714
## my$bctIDCMdiploid0.00025d1H4 0.447657
## my$bctIDCMdiploid0.00025d1H5 0.065659 .
## my$bctIDCMdiploid0.00025d1H6 0.759244
## my$bctIDCMdiploid0.00025d1H7 0.945372
## my$bctIDCMdiploid0.001d1A11 0.047218 *
## my$bctIDCMdiploid0.001d1A2 0.568387
## my$bctIDCMdiploid0.001d1A2B 0.445453
## my$bctIDCMdiploid0.001d1A3 0.982727
## my$bctIDCMdiploid0.001d1A3B 0.774047
## my$bctIDCMdiploid0.001d1A4 0.174949
## my$bctIDCMdiploid0.001d1A4B 0.908856
## my$bctIDCMdiploid0.001d1A5 0.061817 .
## my$bctIDCMdiploid0.001d1A5B 0.726909
## my$bctIDCMdiploid0.001d1A6 0.074694 .
## my$bctIDCMdiploid0.001d1A6B 0.476265
## my$bctIDCMdiploid0.001d1A7 0.001518 **
## my$bctIDCMdiploid0.001d1A8 0.196716
## my$bctIDCMdiploid0.001d1A9 0.044951 *
## my$bctIDCMdiploid0.001d1B1 0.006576 **
## my$bctIDCMdiploid0.001d1B2 0.003039 **
## my$bctIDCMdiploid0.001d1C1 0.002576 **
## my$bctIDCMdiploid0.001d1C2 0.013632 *
## my$bctIDCMdiploid0.001d1D4 0.096546 .
## my$bctIDCMdiploid0.001d1D5 0.152415
## my$bctIDCMdiploid0.001d1D6 0.315226
## my$bctIDCMdiploid0.001d1D7 0.310496
## my$bctIDCMdiploid0.001d1D8 0.240516
## my$bctIDCMdiploid0.001d1E4 0.700111
## my$bctIDCMdiploid0.001d1E5 0.197395
## my$bctIDCMdiploid0.001d1E6 0.023170 *
## my$bctIDCMdiploid0.001d1E7 0.008807 **
## my$bctIDCMdiploid0.001d1E8 0.690775
## my$bctIDCMdiploid0.001d1H11 0.038086 *
## my$bctIDCMdiploid0.001d1H2 0.502150
## my$bctIDCMdiploid0.001d1H2B 0.304231
## my$bctIDCMdiploid0.001d1H3 0.767194
## my$bctIDCMdiploid0.001d1H3B 0.998072
## my$bctIDCMdiploid0.001d1H4 0.721741
## my$bctIDCMdiploid0.001d1H4B 0.915822
## my$bctIDCMdiploid0.001d1H5 0.374428
## my$bctIDCMdiploid0.001d1H5B 0.839864
## my$bctIDCMdiploid0.001d1H6 0.011740 *
## my$bctIDCMdiploid0.001d1H6B 0.055013 .
## my$bctIDCMdiploid0.001d1H7 0.722004
## my$bctIDCMdiploid0.001d1H8 0.534803
## my$bctIDCMdiploid0.001d1H9 0.407275
## my$bctIDCMdiploid0.004d1A2 0.965323
## my$bctIDCMdiploid0.004d1A3 0.522127
## my$bctIDCMdiploid0.004d1A4 0.003677 **
## my$bctIDCMdiploid0.004d1A5 0.004378 **
## my$bctIDCMdiploid0.004d1A6 0.262755
## my$bctIDCMdiploid0.004d1A7 0.153420
## my$bctIDCMdiploid0.004d1B3 0.089106 .
## my$bctIDCMdiploid0.004d1B4 0.060315 .
## my$bctIDCMdiploid0.004d1B5 0.001775 **
## my$bctIDCMdiploid0.004d1B6 0.002307 **
## my$bctIDCMdiploid0.004d1B7 0.00000056011850773 ***
## my$bctIDCMdiploid0.004d1C3 0.003811 **
## my$bctIDCMdiploid0.004d1C4 0.096605 .
## my$bctIDCMdiploid0.004d1C5 0.000868 ***
## my$bctIDCMdiploid0.004d1C6 0.00000684943418923 ***
## my$bctIDCMdiploid0.004d1C7 0.186138
## my$bctIDCMdiploid0.004d1H2 0.335547
## my$bctIDCMdiploid0.004d1H3 0.333005
## my$bctIDCMdiploid0.004d1H4 0.857174
## my$bctIDCMdiploid0.004d1H5 0.001720 **
## my$bctIDCMdiploid0.004d1H6 0.023234 *
## my$bctIDCMdiploid0.004d1H7 0.712242
## my$bctIDCMhaploid0.001d1A2 0.337921
## my$bctIDCMhaploid0.001d1A3 0.034829 *
## my$bctIDCMhaploid0.001d1A3B 0.020091 *
## my$bctIDCMhaploid0.001d1A4 0.00000000002976149 ***
## my$bctIDCMhaploid0.001d1A4B 0.504764
## my$bctIDCMhaploid0.001d1A5 0.00000005766073013 ***
## my$bctIDCMhaploid0.001d1A5B 0.00002392620167394 ***
## my$bctIDCMhaploid0.001d1A6 0.005879 **
## my$bctIDCMhaploid0.001d1A6B 0.002633 **
## my$bctIDCMhaploid0.001d1A7 0.216581
## my$bctIDCMhaploid0.001d1A7B 0.177968
## my$bctIDCMhaploid0.001d1H2 0.000139 ***
## my$bctIDCMhaploid0.001d1H3 0.726659
## my$bctIDCMhaploid0.001d1H3B 0.219285
## my$bctIDCMhaploid0.001d1H4 0.090830 .
## my$bctIDCMhaploid0.001d1H4B 0.465230
## my$bctIDCMhaploid0.001d1H5 0.236513
## my$bctIDCMhaploid0.001d1H5B 0.00000374585588773 ***
## my$bctIDCMhaploid0.001d1H6 0.000766 ***
## my$bctIDCMhaploid0.001d1H6B 0.00000156280464386 ***
## my$bctIDCMhaploid0.001d1H7 0.00002561158235811 ***
## my$bctIDCMhaploid0.001d1H7B 0.00004746151175424 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.922 on 456 degrees of freedom
## Multiple R-squared: 0.7186, Adjusted R-squared: 0.6248
## F-statistic: 7.66 on 152 and 456 DF, p-value: < 0.00000000000000022
myrmse <- sqrt(mean(residuals(mymod)^2)) * 100 # this is the rmse from the fitness equivalence model*100 --> effect size of 1 corresonds to an ~4.307 fitness dif
myeffectsize <- 1/myrmse # effect size of 1 is a 4.307 fitnesss change, so for a fitness change of 1.00, expect effect size of 1/myrmse
# ------------------------------------------------------------------------------
# 250 GEN EXP -- CACULATE POWER TO DETECT TREAT EFF
# ---------------------------
pow <- pwr.f2.test(u = 1, v = ((22 * 2) - 1 - 1), f2 = myeffectsize, sig.level = 0.05)
pow$power # 87.7% power to detect treatment fitness differences of 1.00%
## [1] 0.8774087
# ------------------------------------------------------------------------------
# 250 GEN EXP -- CALCULATE POWER TO DETECT INDIV BC FIT CHANGE
# ----------------- power to detect fitness increase / decrease (t-tests) d
# = m1 - m2 / q; m1 is mean group 1, m2 is mean group 2, q is common
# standard deviation in the two groups here m1 is change in fitness and m2
# is 0. mean q is used and is the weighted mean of all population standard
# deviation of the four replicate sets in each row of the dataset.
my <- myfa_w
my$psd_r1 <- ((my$deltafit - my$mndeltafit)^2)/4
my$psd_r2 <- ((my$deltafit_2 - my$mndeltafit)^2)/4
my$psd_r3 <- ((my$deltafit_3 - my$mndeltafit)^2)/4
my$psd_r4 <- ((my$deltafit_4 - my$mndeltafit)^2)/4
my$psd_m <- rowMeans(cbind(my$psd_r1, my$psd_r2, my$psd_r3, my$psd_r4), na.rm = T)
my$psd <- sqrt(my$psd_m)
psd <- weighted.mean(my$psd, my$mnrdeltafit, na.rm = T)
pow <- pwr.t.test(d = 0.01/psd, n = 4, sig.level = 0.05)
pow # 16% power to detect 1% fit change.
##
## Two-sample t test power calculation
##
## n = 4
## d = 0.7962996
## sig.level = 0.05
## power = 0.1590779
## alternative = two.sided
##
## NOTE: n is number in *each* group
pow <- pwr.t.test(power = 0.8, n = 4, sig.level = 0.05)
pow$d * psd # 80% power to detect ~3% fitness change
## [1] 0.02989775
# ------------------------------------------------------------------------------
rm(my, my1, my10, my2, my3, my4, my5, my6, my7, my8, my9, mymod, pow, i, myeffectsize,
myrmse, psd)Analysis: Power (Vizualize)
# POC DATA
# -----------------------------------------------------------------------------------------
# individual bc
# ----------------------------------------------------------------
my <- poc92 # conduct necessary calculations
my1 <- my[my$exp == 1, ]
colnames(my1) <- paste0(colnames(my1), "_R1")
my2 <- my[my$exp == 2, ]
colnames(my2) <- paste0(colnames(my2), "_R2")
my3 <- my[my$exp == 3, ]
colnames(my3) <- paste0(colnames(my3), "_R3")
my4 <- my[my$exp == 4, ]
colnames(my4) <- paste0(colnames(my4), "_R4")
my5 <- my[my$exp == 5, ]
colnames(my5) <- paste0(colnames(my5), "_R5")
my6 <- my[my$exp == 6, ]
colnames(my6) <- paste0(colnames(my6), "_R6")
my7 <- my[my$exp == 7, ]
colnames(my7) <- paste0(colnames(my7), "_R7")
my8 <- my[my$exp == 8, ]
colnames(my8) <- paste0(colnames(my8), "_R8")
my9 <- my[my$exp == 9, ]
colnames(my9) <- paste0(colnames(my9), "_R9")
my10 <- my[my$exp == 10, ]
colnames(my10) <- paste0(colnames(my10), "_R10")
poc92_w <- cbind(my1, my2, my3, my4, my5, my6, my7, my8, my9, my10)
for (i in 1:nrow(poc92_w)) {
poc92_w$mnrw[i] <- weighted.mean(poc92_w[i, c("rw_R1", "rw_R2", "rw_R3",
"rw_R4", "rw_R5", "rw_R6", "rw_R7", "rw_R8", "rw_R9", "rw_R10")], poc92_w[i,
c("r_R1", "r_R2", "r_R3", "r_R4", "r_R5", "r_R6", "r_R7", "r_R8", "r_R9",
"r_R10")], na.rm = T)
poc92_w$mnr[i] <- rowMeans(poc92_w[i, c("r_R1", "r_R2", "r_R3", "r_R4",
"r_R5", "r_R6", "r_R7", "r_R8", "r_R9", "r_R10")], na.rm = T)
}
my <- poc92_w
my$psd_r1 <- ((my$rw_R1 - my$mnrw)^2)/10
my$psd_r2 <- ((my$rw_R2 - my$mnrw)^2)/10
my$psd_r3 <- ((my$rw_R3 - my$mnrw)^2)/10
my$psd_r4 <- ((my$rw_R4 - my$mnrw)^2)/10
my$psd_r5 <- ((my$rw_R5 - my$mnrw)^2)/10
my$psd_r6 <- ((my$rw_R6 - my$mnrw)^2)/10
my$psd_r7 <- ((my$rw_R7 - my$mnrw)^2)/10
my$psd_r8 <- ((my$rw_R8 - my$mnrw)^2)/10
my$psd_r9 <- ((my$rw_R9 - my$mnrw)^2)/10
my$psd_r10 <- ((my$rw_R10 - my$mnrw)^2)/10
my$psd_m <- rowMeans(cbind(my$psd_r1, my$psd_r2, my$psd_r3, my$psd_r4, my$psd_r5,
my$psd_r6, my$psd_r7, my$psd_r8, my$psd_r9, my$psd_r10), na.rm = T)
my$psd <- sqrt(my$psd_m) # get the pop standard dev for each entry
psd <- weighted.mean(my$psd, my$mnr, na.rm = T) # get the weighted mean pop standard dev for reporting
# prepare plot data ----------------------------------------
mypower <- matrix(nrow = 22 * 3, ncol = 5)
colnames(mypower) <- c("fitchange", "psd", "n", "power", "sig.level")
mypower <- as.data.frame(mypower)
mypower$fitchange <- c(0.0025, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.035,
0.04, 0.045, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.14, 0.16, 0.18,
0.2, 0.22)
mypower$psd <- psd
mypower$n <- c(rep(2, 22), rep(3, 22), rep(4, 22))
mypower$power <- NA
mypower$sig.level <- 0.05
mypower$group <- c(rep(1, 22), rep(2, 22), rep(3, 22))
mypower$group <- as.factor(mypower$group)
for (i in 1:nrow(mypower)) {
pow <- pwr.t.test(d = mypower$fitchange[i]/mypower$psd[i], n = mypower$n[i],
sig.level = mypower$sig.level[i])
mypower$power[i] <- pow$power
}
# model output
# -----------------------------------------------------------------
mypower2 <- round(mypower[, 1:5], digits = 5)
tab_df(mypower2, file = "000_Additional_File_5.html")| fitchange | psd | n | power | sig.level |
|---|---|---|---|---|
| 0.0025 | 0.00494 | 2 | 0.06178 | 0.05 |
| 0.005 | 0.00494 | 2 | 0.09626 | 0.05 |
| 0.01 | 0.00494 | 2 | 0.22197 | 0.05 |
| 0.015 | 0.00494 | 2 | 0.39385 | 0.05 |
| 0.02 | 0.00494 | 2 | 0.57263 | 0.05 |
| 0.025 | 0.00494 | 2 | 0.72731 | 0.05 |
| 0.03 | 0.00494 | 2 | 0.84254 | 0.05 |
| 0.035 | 0.00494 | 2 | 0.91772 | 0.05 |
| 0.04 | 0.00494 | 2 | 0.96109 | 0.05 |
| 0.045 | 0.00494 | 2 | 0.98335 | 0.05 |
| 0.05 | 0.00494 | 2 | 0.99355 | 0.05 |
| 0.06 | 0.00494 | 2 | 0.99928 | 0.05 |
| 0.07 | 0.00494 | 2 | 0.99995 | 0.05 |
| 0.08 | 0.00494 | 2 | 1 | 0.05 |
| 0.09 | 0.00494 | 2 | 1 | 0.05 |
| 0.1 | 0.00494 | 2 | 1 | 0.05 |
| 0.12 | 0.00494 | 2 | 1 | 0.05 |
| 0.14 | 0.00494 | 2 | 1 | 0.05 |
| 0.16 | 0.00494 | 2 | 1 | 0.05 |
| 0.18 | 0.00494 | 2 | 1 | 0.05 |
| 0.2 | 0.00494 | 2 | 1 | 0.05 |
| 0.22 | 0.00494 | 2 | 1 | 0.05 |
| 0.0025 | 0.00494 | 3 | 0.0775 | 0.05 |
| 0.005 | 0.00494 | 3 | 0.16143 | 0.05 |
| 0.01 | 0.00494 | 3 | 0.47113 | 0.05 |
| 0.015 | 0.00494 | 3 | 0.79151 | 0.05 |
| 0.02 | 0.00494 | 3 | 0.95204 | 0.05 |
| 0.025 | 0.00494 | 3 | 0.99369 | 0.05 |
| 0.03 | 0.00494 | 3 | 0.99953 | 0.05 |
| 0.035 | 0.00494 | 3 | 0.99998 | 0.05 |
| 0.04 | 0.00494 | 3 | 1 | 0.05 |
| 0.045 | 0.00494 | 3 | 1 | 0.05 |
| 0.05 | 0.00494 | 3 | 1 | 0.05 |
| 0.06 | 0.00494 | 3 | 1 | 0.05 |
| 0.07 | 0.00494 | 3 | 1 | 0.05 |
| 0.08 | 0.00494 | 3 | 1 | 0.05 |
| 0.09 | 0.00494 | 3 | 1 | 0.05 |
| 0.1 | 0.00494 | 3 | 1 | 0.05 |
| 0.12 | 0.00494 | 3 | 1 | 0.05 |
| 0.14 | 0.00494 | 3 | 1 | 0.05 |
| 0.16 | 0.00494 | 3 | 1 | 0.05 |
| 0.18 | 0.00494 | 3 | 1 | 0.05 |
| 0.2 | 0.00494 | 3 | 1 | 0.05 |
| 0.22 | 0.00494 | 3 | 1 | 0.05 |
| 0.0025 | 0.00494 | 4 | 0.09332 | 0.05 |
| 0.005 | 0.00494 | 4 | 0.22739 | 0.05 |
| 0.01 | 0.00494 | 4 | 0.66699 | 0.05 |
| 0.015 | 0.00494 | 4 | 0.94369 | 0.05 |
| 0.02 | 0.00494 | 4 | 0.99672 | 0.05 |
| 0.025 | 0.00494 | 4 | 0.99994 | 0.05 |
| 0.03 | 0.00494 | 4 | 1 | 0.05 |
| 0.035 | 0.00494 | 4 | 1 | 0.05 |
| 0.04 | 0.00494 | 4 | 1 | 0.05 |
| 0.045 | 0.00494 | 4 | 1 | 0.05 |
| 0.05 | 0.00494 | 4 | 1 | 0.05 |
| 0.06 | 0.00494 | 4 | 1 | 0.05 |
| 0.07 | 0.00494 | 4 | 1 | 0.05 |
| 0.08 | 0.00494 | 4 | 1 | 0.05 |
| 0.09 | 0.00494 | 4 | 1 | 0.05 |
| 0.1 | 0.00494 | 4 | 1 | 0.05 |
| 0.12 | 0.00494 | 4 | 1 | 0.05 |
| 0.14 | 0.00494 | 4 | 1 | 0.05 |
| 0.16 | 0.00494 | 4 | 1 | 0.05 |
| 0.18 | 0.00494 | 4 | 1 | 0.05 |
| 0.2 | 0.00494 | 4 | 1 | 0.05 |
| 0.22 | 0.00494 | 4 | 1 | 0.05 |
# -----------------------------------------------------------------------------
# Plotting ------------------------------------------------- plot effect
# size x power -------------
mypower$n <- factor(mypower$n, levels = c(4, 3, 2))
p <- ggplot(mypower, aes(x = fitchange, y = power, group = n)) + geom_vline(xintercept = 0.01,
linetype = "dashed", color = "gray") + geom_vline(xintercept = 0.025, linetype = "dashed",
color = "gray") + geom_vline(xintercept = 0.05, linetype = "dashed", color = "gray") +
geom_vline(xintercept = 0.1, linetype = "dashed", color = "gray") + geom_line(aes(color = n)) +
geom_point(aes(color = n)) + geom_point(x = 0.01, y = pwr.t.test(d = 0.01/psd,
n = 4, sig.level = 0.05)$power, pch = 1, size = 5, color = "gray") + geom_point(x = 0.025,
y = pwr.t.test(d = 0.025/psd, n = 4, sig.level = 0.05)$power, pch = 1, size = 5,
color = "gray") + geom_point(x = 0.05, y = pwr.t.test(d = 0.05/psd, n = 4,
sig.level = 0.05)$power, pch = 1, size = 5, color = "gray") + geom_point(x = 0.1,
y = pwr.t.test(d = 0.1/psd, n = 4, sig.level = 0.05)$power, pch = 1, size = 5,
color = "gray") + scale_x_continuous(name = "", breaks = c(0.01, 0.025,
0.05, 0.1), labels = c("1%", "2.5%", "5%", "10%"), limits = c(0, 0.1)) +
labs(color = "n (replicates \n per timepoint)") + theme_classic() + labs(tag = "A") +
theme(legend.position = "none") + theme(axis.text.x = element_text(angle = 45,
hjust = 1))
p
## Warning: Removed 18 rows containing missing values (geom_path).
## Warning: Removed 18 rows containing missing values (geom_point).
pA <- p
# treatment
# -------------------------------------------------------------------- run
# the appropriate model (fit equivalence model from above -- telling us
# ability to measure fit for each bcid based on our 10 replicates.)
my <- poc92[order(poc92$bcid), ]
my$rw_corrected <- my$rw - weighted.mean(my$rw, my$r)
mymod <- lm(my$rw_corrected ~ my$bcid + 0, weights = my$r)
summary(mymod)
##
## Call:
## lm(formula = my$rw_corrected ~ my$bcid + 0, weights = my$r)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -4.7350 -0.5858 -0.0109 0.5304 5.3405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## my$bcidd1A10 0.00132555 0.00371437 0.357 0.721280
## my$bcidd1A11 -0.00762370 0.00466055 -1.636 0.102267
## my$bcidd1A2 0.00811450 0.00396019 2.049 0.040778 *
## my$bcidd1A3 0.00494242 0.00439553 1.124 0.261165
## my$bcidd1A4 0.00795573 0.00448555 1.774 0.076495 .
## my$bcidd1A5 0.00103320 0.00520070 0.199 0.842573
## my$bcidd1A6 -0.00085773 0.00357809 -0.240 0.810610
## my$bcidd1A7 0.00344369 0.00512097 0.672 0.501476
## my$bcidd1A8 0.00583557 0.00414724 1.407 0.159778
## my$bcidd1A9 0.00724382 0.00374750 1.933 0.053584 .
## my$bcidd1B1 -0.00527477 0.00502129 -1.050 0.293807
## my$bcidd1B10 -0.00821202 0.00615734 -1.334 0.182674
## my$bcidd1B11 -0.01330702 0.00511428 -2.602 0.009437 **
## my$bcidd1B12 -0.00741678 0.00553232 -1.341 0.180413
## my$bcidd1B2 0.00289719 0.00431315 0.672 0.501957
## my$bcidd1B3 0.00354456 0.00434513 0.816 0.414878
## my$bcidd1B4 0.00951091 0.00421461 2.257 0.024293 *
## my$bcidd1B5 0.00961576 0.00421606 2.281 0.022819 *
## my$bcidd1B6 0.00134667 0.00501745 0.268 0.788462
## my$bcidd1B7 -0.00398474 0.00435334 -0.915 0.360287
## my$bcidd1B8 0.00386754 0.00435584 0.888 0.374856
## my$bcidd1B9 -0.00197406 0.00517408 -0.382 0.702909
## my$bcidd1C1 -0.00130215 0.00523917 -0.249 0.803778
## my$bcidd1C10 -0.00200878 0.00612990 -0.328 0.743221
## my$bcidd1C11 0.00332553 0.00513862 0.647 0.517707
## my$bcidd1C12 -0.00495638 0.00525931 -0.942 0.346266
## my$bcidd1C2 0.00310014 0.00440966 0.703 0.482235
## my$bcidd1C3 0.00170870 0.00544151 0.314 0.753591
## my$bcidd1C4 0.00064326 0.00445091 0.145 0.885124
## my$bcidd1C5 -0.00293377 0.00470909 -0.623 0.533457
## my$bcidd1C6 -0.00108375 0.00401081 -0.270 0.787069
## my$bcidd1C7 -0.00224296 0.00857907 -0.261 0.793814
## my$bcidd1C8 -0.00815331 0.00535937 -1.521 0.128566
## my$bcidd1C9 0.01983075 0.00598788 3.312 0.000968 ***
## my$bcidd1D1 0.00371456 0.00415556 0.894 0.371651
## my$bcidd1D10 0.01036458 0.00405675 2.555 0.010802 *
## my$bcidd1D11 -0.00004981 0.00380942 -0.013 0.989570
## my$bcidd1D12 0.00301900 0.00386910 0.780 0.435449
## my$bcidd1D2 -0.01098378 0.02236414 -0.491 0.623464
## my$bcidd1D3 -0.00440055 0.00562205 -0.783 0.434011
## my$bcidd1D4 -0.00693858 0.00737022 -0.941 0.346760
## my$bcidd1D5 -0.00326940 0.00563709 -0.580 0.562087
## my$bcidd1D6 -0.00844038 0.00554061 -1.523 0.128053
## my$bcidd1D7 -0.00168313 0.00462568 -0.364 0.716052
## my$bcidd1D8 0.00180750 0.00397535 0.455 0.649462
## my$bcidd1D9 0.00014080 0.00545635 0.026 0.979419
## my$bcidd1E1 -0.01270595 0.00523534 -2.427 0.015441 *
## my$bcidd1E10 -0.00147548 0.00433537 -0.340 0.733691
## my$bcidd1E11 -0.00708010 0.00406068 -1.744 0.081609 .
## my$bcidd1E12 0.01157108 0.00442433 2.615 0.009078 **
## my$bcidd1E2 0.00500682 0.00373187 1.342 0.180085
## my$bcidd1E3 0.00762603 0.00554298 1.376 0.169259
## my$bcidd1E4 0.00591987 0.00384425 1.540 0.123964
## my$bcidd1E5 -0.00657763 0.00498803 -1.319 0.187644
## my$bcidd1E6 -0.00139332 0.00372470 -0.374 0.708444
## my$bcidd1E7 0.00852322 0.00396829 2.148 0.032020 *
## my$bcidd1E8 -0.00369015 0.00443221 -0.833 0.405327
## my$bcidd1E9 0.00639603 0.00436339 1.466 0.143076
## my$bcidd1F1 -0.00012498 0.00381935 -0.033 0.973904
## my$bcidd1F10 0.00393912 0.00423405 0.930 0.352467
## my$bcidd1F11 0.00670256 0.00469224 1.428 0.153547
## my$bcidd1F12 0.00778245 0.00385357 2.020 0.043756 *
## my$bcidd1F2 0.00582870 0.00435141 1.339 0.180780
## my$bcidd1F3 0.00651265 0.00596554 1.092 0.275281
## my$bcidd1F4 -0.02625366 0.00923796 -2.842 0.004596 **
## my$bcidd1F5 -0.04834088 0.00429859 -11.246 < 0.0000000000000002 ***
## my$bcidd1F6 -0.00012930 0.00381509 -0.034 0.972972
## my$bcidd1F7 -0.01483489 0.00492987 -3.009 0.002700 **
## my$bcidd1F8 -0.00220117 0.00605837 -0.363 0.716455
## my$bcidd1F9 0.00029767 0.01166664 0.026 0.979651
## my$bcidd1G1 0.01006985 0.00654635 1.538 0.124376
## my$bcidd1G10 0.00690433 0.00358418 1.926 0.054408 .
## my$bcidd1G11 0.00299309 0.00557059 0.537 0.591205
## my$bcidd1G12 0.00147423 0.00344561 0.428 0.668867
## my$bcidd1G2 0.00615615 0.00569582 1.081 0.280096
## my$bcidd1G3 0.00097634 0.00390830 0.250 0.802795
## my$bcidd1G4 -0.00335673 0.00478773 -0.701 0.483433
## my$bcidd1G5 -0.01510445 0.00537164 -2.812 0.005043 **
## my$bcidd1G6 -0.00650477 0.00473025 -1.375 0.169463
## my$bcidd1G7 -0.00911754 0.00430618 -2.117 0.034534 *
## my$bcidd1G8 -0.00467010 0.00489124 -0.955 0.339967
## my$bcidd1G9 -0.00345997 0.00470652 -0.735 0.462463
## my$bcidd1H11 0.00188567 0.00387909 0.486 0.627018
## my$bcidd1H2 -0.00688077 0.00535513 -1.285 0.199193
## my$bcidd1H3 -0.00074760 0.00640698 -0.117 0.907138
## my$bcidd1H4 -0.03016766 0.00925002 -3.261 0.001155 **
## my$bcidd1H5 -0.00289320 0.00438300 -0.660 0.509377
## my$bcidd1H6 0.00117090 0.00365259 0.321 0.748621
## my$bcidd1H7 -0.00459377 0.00852914 -0.539 0.590312
## my$bcidd1H8 -0.00449698 0.00614172 -0.732 0.464255
## my$bcidd1H9 -0.00798745 0.00584794 -1.366 0.172359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.127 on 819 degrees of freedom
## Multiple R-squared: 0.2653, Adjusted R-squared: 0.1836
## F-statistic: 3.25 on 91 and 819 DF, p-value: < 0.00000000000000022
myrmse <- sqrt(mean(residuals(mymod)^2)) * 100 # this is the rmse from the fitness equivalence model*100 --> effect size of 1 corresonds to an ~1.76 fitness change
myeffectsize <- 1/myrmse # effect size of 1 is a 1.76 fitnesss change, so for a fitness change of 1.00, expect effect size of 1/myrmse
myv <- (22 * 2) - 1 - 1
mypowersize <- (pwr.f2.test(u = 1, v = myv, f2 = myeffectsize, sig.level = 0.05))$power # powersize for plotting below, where 22 is the number of BCs in any given treatment.
# prepare plot data ----------
mypower <- matrix(nrow = 100, ncol = 5)
colnames(mypower) <- c("u", "v", "power", "f2", "sig.level")
mypower <- as.data.frame(mypower)
mypower$u <- 1
mypower$v <- c(myv, myv * 0.9, myv * 0.8, myv * 0.7, myv * 0.6, myv * 0.5, myv *
0.4, myv * 0.3, myv * 0.2, myv * 0.1)
mypower$power <- NA
mypower$f2 <- c(rep(myeffectsize * 2, 10), rep(myeffectsize * 1.75, 10), rep(myeffectsize *
1.5, 10), rep(myeffectsize * 1.25, 10), rep(myeffectsize * 1, 10), rep(myeffectsize *
0.75, 10), rep(myeffectsize * 0.5, 10), rep(myeffectsize * 0.25, 10), rep(myeffectsize *
0.1, 10), rep(myeffectsize * 0.05, 10))
mypower$sig.level <- 0.05
mypower$group <- c(rep(1, 10), rep(2, 10), rep(3, 10), rep(4, 10), rep(5, 10),
rep(6, 10), rep(7, 10), rep(8, 10), rep(9, 10), rep(10, 10))
mypower$group <- as.factor(mypower$group)
for (i in 1:nrow(mypower)) {
pow <- pwr.f2.test(u = mypower$u[i], v = mypower$v[i], f2 = mypower$f2[i],
sig.level = mypower$sig.level[i])
mypower$power[i] <- pow$power
}
# model output
# -----------------------------------------------------------------
mypower2 <- round(mypower[, 1:5], digits = 5)
tab_df(mypower2, file = "000_Additional_File_6.html")| u | v | power | f2 | sig.level |
|---|---|---|---|---|
| 1 | 42 | 1 | 1.13786 | 0.05 |
| 1 | 37.8 | 1 | 1.13786 | 0.05 |
| 1 | 33.6 | 0.99999 | 1.13786 | 0.05 |
| 1 | 29.4 | 0.99993 | 1.13786 | 0.05 |
| 1 | 25.2 | 0.99964 | 1.13786 | 0.05 |
| 1 | 21 | 0.9982 | 1.13786 | 0.05 |
| 1 | 16.8 | 0.99152 | 1.13786 | 0.05 |
| 1 | 12.6 | 0.96323 | 1.13786 | 0.05 |
| 1 | 8.4 | 0.85736 | 1.13786 | 0.05 |
| 1 | 4.2 | 0.53379 | 1.13786 | 0.05 |
| 1 | 42 | 1 | 0.99563 | 0.05 |
| 1 | 37.8 | 0.99998 | 0.99563 | 0.05 |
| 1 | 33.6 | 0.99993 | 0.99563 | 0.05 |
| 1 | 29.4 | 0.99971 | 0.99563 | 0.05 |
| 1 | 25.2 | 0.99881 | 0.99563 | 0.05 |
| 1 | 21 | 0.9953 | 0.99563 | 0.05 |
| 1 | 16.8 | 0.98249 | 0.99563 | 0.05 |
| 1 | 12.6 | 0.93932 | 0.99563 | 0.05 |
| 1 | 8.4 | 0.80939 | 0.99563 | 0.05 |
| 1 | 4.2 | 0.48329 | 0.99563 | 0.05 |
| 1 | 42 | 0.99997 | 0.85339 | 0.05 |
| 1 | 37.8 | 0.9999 | 0.85339 | 0.05 |
| 1 | 33.6 | 0.99965 | 0.85339 | 0.05 |
| 1 | 29.4 | 0.99882 | 0.85339 | 0.05 |
| 1 | 25.2 | 0.99618 | 0.85339 | 0.05 |
| 1 | 21 | 0.98809 | 0.85339 | 0.05 |
| 1 | 16.8 | 0.96467 | 0.85339 | 0.05 |
| 1 | 12.6 | 0.90157 | 0.85339 | 0.05 |
| 1 | 8.4 | 0.74793 | 0.85339 | 0.05 |
| 1 | 4.2 | 0.42907 | 0.85339 | 0.05 |
| 1 | 42 | 0.99977 | 0.71116 | 0.05 |
| 1 | 37.8 | 0.99936 | 0.71116 | 0.05 |
| 1 | 33.6 | 0.99827 | 0.71116 | 0.05 |
| 1 | 29.4 | 0.99542 | 0.71116 | 0.05 |
| 1 | 25.2 | 0.98825 | 0.71116 | 0.05 |
| 1 | 21 | 0.97084 | 0.71116 | 0.05 |
| 1 | 16.8 | 0.93065 | 0.71116 | 0.05 |
| 1 | 12.6 | 0.84354 | 0.71116 | 0.05 |
| 1 | 8.4 | 0.67065 | 0.71116 | 0.05 |
| 1 | 4.2 | 0.3713 | 0.71116 | 0.05 |
| 1 | 42 | 0.99828 | 0.56893 | 0.05 |
| 1 | 37.8 | 0.99626 | 0.56893 | 0.05 |
| 1 | 33.6 | 0.99199 | 0.56893 | 0.05 |
| 1 | 29.4 | 0.98321 | 0.56893 | 0.05 |
| 1 | 25.2 | 0.96562 | 0.56893 | 0.05 |
| 1 | 21 | 0.93157 | 0.56893 | 0.05 |
| 1 | 16.8 | 0.86837 | 0.56893 | 0.05 |
| 1 | 12.6 | 0.75723 | 0.56893 | 0.05 |
| 1 | 8.4 | 0.57569 | 0.56893 | 0.05 |
| 1 | 4.2 | 0.31033 | 0.56893 | 0.05 |
| 1 | 42 | 0.98846 | 0.4267 | 0.05 |
| 1 | 37.8 | 0.98002 | 0.4267 | 0.05 |
| 1 | 33.6 | 0.96592 | 0.4267 | 0.05 |
| 1 | 29.4 | 0.94277 | 0.4267 | 0.05 |
| 1 | 25.2 | 0.90564 | 0.4267 | 0.05 |
| 1 | 21 | 0.84773 | 0.4267 | 0.05 |
| 1 | 16.8 | 0.76039 | 0.4267 | 0.05 |
| 1 | 12.6 | 0.63426 | 0.4267 | 0.05 |
| 1 | 8.4 | 0.46235 | 0.4267 | 0.05 |
| 1 | 4.2 | 0.24671 | 0.4267 | 0.05 |
| 1 | 42 | 0.93266 | 0.28446 | 0.05 |
| 1 | 37.8 | 0.90628 | 0.28446 | 0.05 |
| 1 | 33.6 | 0.87079 | 0.28446 | 0.05 |
| 1 | 29.4 | 0.82377 | 0.28446 | 0.05 |
| 1 | 25.2 | 0.7625 | 0.28446 | 0.05 |
| 1 | 21 | 0.68427 | 0.28446 | 0.05 |
| 1 | 16.8 | 0.58681 | 0.28446 | 0.05 |
| 1 | 12.6 | 0.46905 | 0.28446 | 0.05 |
| 1 | 8.4 | 0.33223 | 0.28446 | 0.05 |
| 1 | 4.2 | 0.18127 | 0.28446 | 0.05 |
| 1 | 42 | 0.68587 | 0.14223 | 0.05 |
| 1 | 37.8 | 0.64003 | 0.14223 | 0.05 |
| 1 | 33.6 | 0.58928 | 0.14223 | 0.05 |
| 1 | 29.4 | 0.53353 | 0.14223 | 0.05 |
| 1 | 25.2 | 0.47288 | 0.14223 | 0.05 |
| 1 | 21 | 0.40761 | 0.14223 | 0.05 |
| 1 | 16.8 | 0.33826 | 0.14223 | 0.05 |
| 1 | 12.6 | 0.26565 | 0.14223 | 0.05 |
| 1 | 8.4 | 0.19088 | 0.14223 | 0.05 |
| 1 | 4.2 | 0.11516 | 0.14223 | 0.05 |
| 1 | 42 | 0.33969 | 0.05689 | 0.05 |
| 1 | 37.8 | 0.31119 | 0.05689 | 0.05 |
| 1 | 33.6 | 0.28227 | 0.05689 | 0.05 |
| 1 | 29.4 | 0.25301 | 0.05689 | 0.05 |
| 1 | 25.2 | 0.22351 | 0.05689 | 0.05 |
| 1 | 21 | 0.19387 | 0.05689 | 0.05 |
| 1 | 16.8 | 0.16421 | 0.05689 | 0.05 |
| 1 | 12.6 | 0.13464 | 0.05689 | 0.05 |
| 1 | 8.4 | 0.10521 | 0.05689 | 0.05 |
| 1 | 4.2 | 0.07583 | 0.05689 | 0.05 |
| 1 | 42 | 0.1942 | 0.02845 | 0.05 |
| 1 | 37.8 | 0.17941 | 0.02845 | 0.05 |
| 1 | 33.6 | 0.16465 | 0.02845 | 0.05 |
| 1 | 29.4 | 0.14993 | 0.02845 | 0.05 |
| 1 | 25.2 | 0.13525 | 0.02845 | 0.05 |
| 1 | 21 | 0.12064 | 0.02845 | 0.05 |
| 1 | 16.8 | 0.10612 | 0.02845 | 0.05 |
| 1 | 12.6 | 0.09168 | 0.02845 | 0.05 |
| 1 | 8.4 | 0.0773 | 0.02845 | 0.05 |
| 1 | 4.2 | 0.06286 | 0.02845 | 0.05 |
# -----------------------------------------------------------------------------
# plot -----------------------
myheight <- 7
mywidth <- 7
mypower$n <- mypower$v + 2
mypower$n <- factor(mypower$n, levels = c(myv + 2, (myv * 0.9) + 2, (myv * 0.8) +
2, (myv * 0.7) + 2, (myv * 0.6) + 2, (myv * 0.5) + 2, (myv * 0.4) + 2, (myv *
0.3) + 2, (myv * 0.2) + 2, (myv * 0.1) + 2))
mypower$v <- factor(mypower$v, levels = c(myv, myv * 0.9, myv * 0.8, myv * 0.7,
myv * 0.6, myv * 0.5, myv * 0.4, myv * 0.3, myv * 0.2, myv * 0.1))
p <- ggplot(mypower, aes(x = f2, y = power, group = n)) + geom_vline(xintercept = myeffectsize,
linetype = "dashed", color = "darkgray") + geom_line(aes(color = n)) + geom_point(aes(color = n)) +
geom_point(x = myeffectsize, y = mypowersize, pch = 1, size = 5, color = "darkgray") +
theme_classic() + labs(tag = "B") + labs(color = "n (total barcodes)") +
scale_x_continuous(name = "", breaks = c(myeffectsize * 0.05, myeffectsize *
0.1, myeffectsize * 0.25, myeffectsize * 0.5, myeffectsize * 0.75, myeffectsize,
myeffectsize * 1.25, myeffectsize * 1.5, myeffectsize * 1.75, myeffectsize *
2), labels = c("0.05%", "", "0.25%", "0.5%", "0.75%", "1.0%", "1.25%",
"1.5%", "1.75%", "2.0%")) + theme(legend.position = "none") + theme(axis.text.x = element_text(angle = 45,
hjust = 1))
p
pB <- p
# 250gen DATA
# -----------------------------------------------------------------------------------------
# individual BC
# ---------------------------------------------------------------- power to
# detect fitness increase / decrease (t-tests)
# -------------------------------------------- d = m1 - m2 / q; m1 is mean
# group 1, m2 is mean group 2, q is common standard deviation in the two
# groups here m1 is change in fitness and m2 is 0. mean q is used and is the
# weighted mean of all population standard deviation of the four replicate
# sets in each row of the dataset.
# get data and conduct population sd calculation -----------
my <- myfa_w
my$psd_r1 <- (my$deltafit - my$mndeltafit)^2
my$psd_r2 <- (my$deltafit_2 - my$mndeltafit)^2
my$psd_r3 <- (my$deltafit_3 - my$mndeltafit)^2
my$psd_r4 <- (my$deltafit_4 - my$mndeltafit)^2
my$psd_m <- rowMeans(cbind(my$psd_r1, my$psd_r2, my$psd_r3, my$psd_r4), na.rm = T)
my$psd <- sqrt(my$psd_m)
psd <- weighted.mean(my$psd, my$mnrdeltafit, na.rm = T)
# prepare plot data ----------------------------------------
mypower <- matrix(nrow = 22 * 3, ncol = 5)
colnames(mypower) <- c("fitchange", "psd", "n", "power", "sig.level")
mypower <- as.data.frame(mypower)
mypower$fitchange <- c(0.0025, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.035,
0.04, 0.045, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.14, 0.16, 0.18,
0.2, 0.22)
mypower$psd <- psd
mypower$n <- c(rep(2, 22), rep(3, 22), rep(4, 22))
mypower$power <- NA
mypower$sig.level <- 0.05
mypower$group <- c(rep(1, 22), rep(2, 22), rep(3, 22))
mypower$group <- as.factor(mypower$group)
for (i in 1:nrow(mypower)) {
pow <- pwr.t.test(d = mypower$fitchange[i]/mypower$psd[i], n = mypower$n[i],
sig.level = mypower$sig.level[i])
mypower$power[i] <- pow$power
}
# model output
# -----------------------------------------------------------------
mypower2 <- round(mypower[, 1:5], digits = 5)
tab_df(mypower2, file = "000_Additional_File_9.html")| fitchange | psd | n | power | sig.level |
|---|---|---|---|---|
| 0.0025 | 0.02512 | 2 | 0.05046 | 0.05 |
| 0.005 | 0.02512 | 2 | 0.05183 | 0.05 |
| 0.01 | 0.02512 | 2 | 0.05731 | 0.05 |
| 0.015 | 0.02512 | 2 | 0.06638 | 0.05 |
| 0.02 | 0.02512 | 2 | 0.07892 | 0.05 |
| 0.025 | 0.02512 | 2 | 0.09479 | 0.05 |
| 0.03 | 0.02512 | 2 | 0.11383 | 0.05 |
| 0.035 | 0.02512 | 2 | 0.13581 | 0.05 |
| 0.04 | 0.02512 | 2 | 0.16049 | 0.05 |
| 0.045 | 0.02512 | 2 | 0.18762 | 0.05 |
| 0.05 | 0.02512 | 2 | 0.2169 | 0.05 |
| 0.06 | 0.02512 | 2 | 0.28072 | 0.05 |
| 0.07 | 0.02512 | 2 | 0.34947 | 0.05 |
| 0.08 | 0.02512 | 2 | 0.42067 | 0.05 |
| 0.09 | 0.02512 | 2 | 0.49199 | 0.05 |
| 0.1 | 0.02512 | 2 | 0.56137 | 0.05 |
| 0.12 | 0.02512 | 2 | 0.6878 | 0.05 |
| 0.14 | 0.02512 | 2 | 0.79112 | 0.05 |
| 0.16 | 0.02512 | 2 | 0.86862 | 0.05 |
| 0.18 | 0.02512 | 2 | 0.92232 | 0.05 |
| 0.2 | 0.02512 | 2 | 0.95683 | 0.05 |
| 0.22 | 0.02512 | 2 | 0.97744 | 0.05 |
| 0.0025 | 0.02512 | 3 | 0.05106 | 0.05 |
| 0.005 | 0.02512 | 3 | 0.05423 | 0.05 |
| 0.01 | 0.02512 | 3 | 0.06698 | 0.05 |
| 0.015 | 0.02512 | 3 | 0.08841 | 0.05 |
| 0.02 | 0.02512 | 3 | 0.11868 | 0.05 |
| 0.025 | 0.02512 | 3 | 0.15778 | 0.05 |
| 0.03 | 0.02512 | 3 | 0.20539 | 0.05 |
| 0.035 | 0.02512 | 3 | 0.26078 | 0.05 |
| 0.04 | 0.02512 | 3 | 0.32273 | 0.05 |
| 0.045 | 0.02512 | 3 | 0.3896 | 0.05 |
| 0.05 | 0.02512 | 3 | 0.45936 | 0.05 |
| 0.06 | 0.02512 | 3 | 0.59886 | 0.05 |
| 0.07 | 0.02512 | 3 | 0.72487 | 0.05 |
| 0.08 | 0.02512 | 3 | 0.82621 | 0.05 |
| 0.09 | 0.02512 | 3 | 0.89913 | 0.05 |
| 0.1 | 0.02512 | 3 | 0.94628 | 0.05 |
| 0.12 | 0.02512 | 3 | 0.98825 | 0.05 |
| 0.14 | 0.02512 | 3 | 0.99819 | 0.05 |
| 0.16 | 0.02512 | 3 | 0.9998 | 0.05 |
| 0.18 | 0.02512 | 3 | 0.99998 | 0.05 |
| 0.2 | 0.02512 | 3 | 1 | 0.05 |
| 0.22 | 0.02512 | 3 | 1 | 0.05 |
| 0.0025 | 0.02512 | 4 | 0.05165 | 0.05 |
| 0.005 | 0.02512 | 4 | 0.05661 | 0.05 |
| 0.01 | 0.02512 | 4 | 0.07666 | 0.05 |
| 0.015 | 0.02512 | 4 | 0.11068 | 0.05 |
| 0.02 | 0.02512 | 4 | 0.15908 | 0.05 |
| 0.025 | 0.02512 | 4 | 0.22158 | 0.05 |
| 0.03 | 0.02512 | 4 | 0.29675 | 0.05 |
| 0.035 | 0.02512 | 4 | 0.38181 | 0.05 |
| 0.04 | 0.02512 | 4 | 0.47272 | 0.05 |
| 0.045 | 0.02512 | 4 | 0.56471 | 0.05 |
| 0.05 | 0.02512 | 4 | 0.65294 | 0.05 |
| 0.06 | 0.02512 | 4 | 0.8026 | 0.05 |
| 0.07 | 0.02512 | 4 | 0.90401 | 0.05 |
| 0.08 | 0.02512 | 4 | 0.96036 | 0.05 |
| 0.09 | 0.02512 | 4 | 0.98615 | 0.05 |
| 0.1 | 0.02512 | 4 | 0.99592 | 0.05 |
| 0.12 | 0.02512 | 4 | 0.99979 | 0.05 |
| 0.14 | 0.02512 | 4 | 0.99999 | 0.05 |
| 0.16 | 0.02512 | 4 | 1 | 0.05 |
| 0.18 | 0.02512 | 4 | 1 | 0.05 |
| 0.2 | 0.02512 | 4 | 1 | 0.05 |
| 0.22 | 0.02512 | 4 | 1 | 0.05 |
# -----------------------------------------------------------------------------
# Plotting ------------------------------------------------- plot effect
# size x power -------------
mypower$n <- factor(mypower$n, levels = c(4, 3, 2))
p <- ggplot(mypower, aes(x = fitchange, y = power, group = n)) + geom_vline(xintercept = 0.01,
linetype = "dashed", color = "gray") + geom_vline(xintercept = 0.025, linetype = "dashed",
color = "gray") + geom_vline(xintercept = 0.05, linetype = "dashed", color = "gray") +
geom_vline(xintercept = 0.1, linetype = "dashed", color = "gray") + geom_line(aes(color = n)) +
geom_point(aes(color = n)) + geom_point(x = 0.01, y = pwr.t.test(d = 0.01/psd,
n = 4, sig.level = 0.05)$power, pch = 1, size = 5, color = "gray") + geom_point(x = 0.025,
y = pwr.t.test(d = 0.025/psd, n = 4, sig.level = 0.05)$power, pch = 1, size = 5,
color = "gray") + geom_point(x = 0.05, y = pwr.t.test(d = 0.05/psd, n = 4,
sig.level = 0.05)$power, pch = 1, size = 5, color = "gray") + geom_point(x = 0.1,
y = pwr.t.test(d = 0.1/psd, n = 4, sig.level = 0.05)$power, pch = 1, size = 5,
color = "gray") + scale_x_continuous(name = "Fitness Change", breaks = c(0.01,
0.025, 0.05, 0.1), labels = c("1%", "2.5%", "5%", "10%"), limits = c(0,
0.1)) + labs(color = "n (replicates \n per timepoint)") + theme_classic() +
labs(tag = "C") + theme(legend.direction = "horizontal") + theme(axis.text.x = element_text(angle = 45,
hjust = 1))
legend1 <- get_legend(p)
## Warning: Removed 18 rows containing missing values (geom_path).
## Warning: Removed 18 rows containing missing values (geom_point).
p <- p + theme(legend.position = "none")
pC <- p
# treatment
# -------------------------------------------------------------------- run
# the appropriate model (fit equivalence model from above -- telling us
# ability to measure fit for each bcid based on our 10 replicates.)
my <- myfa[order(myfa$bctID), ]
mymod <- lm(my$deltafit ~ my$bctID + 0, weights = my$rdeltafit)
summary(mymod)
##
## Call:
## lm(formula = my$deltafit ~ my$bctID + 0, weights = my$rdeltafit)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -8.9024 -0.9879 0.0734 0.9561 4.8229
##
## Coefficients:
## Estimate Std. Error t value
## my$bctIDCM+Ethanoldiploid0.001d1A2 0.00224666 0.01223756 0.184
## my$bctIDCM+Ethanoldiploid0.001d1A4 0.01765500 0.01365937 1.293
## my$bctIDCM+Ethanoldiploid0.001d1A5 -0.00183138 0.01601185 -0.114
## my$bctIDCM+Ethanoldiploid0.001d1A6 0.00847721 0.01160185 0.731
## my$bctIDCM+Ethanoldiploid0.001d1A7 0.00837054 0.01577339 0.531
## my$bctIDCM+Ethanoldiploid0.001d1D10 0.03354979 0.01054805 3.181
## my$bctIDCM+Ethanoldiploid0.001d1D11 0.02980938 0.01093362 2.726
## my$bctIDCM+Ethanoldiploid0.001d1D12 0.02399057 0.01018367 2.356
## my$bctIDCM+Ethanoldiploid0.001d1D9 0.02343344 0.02027118 1.156
## my$bctIDCM+Ethanoldiploid0.001d1E10 0.04807661 0.01120179 4.292
## my$bctIDCM+Ethanoldiploid0.001d1E11 0.03162314 0.01125755 2.809
## my$bctIDCM+Ethanoldiploid0.001d1E12 -0.03049231 0.01211023 -2.518
## my$bctIDCM+Ethanoldiploid0.001d1E9 -0.00999714 0.01070592 -0.934
## my$bctIDCM+Ethanoldiploid0.001d1F1 0.00938513 0.00879011 1.068
## my$bctIDCM+Ethanoldiploid0.001d1F2 0.01938389 0.01121524 1.728
## my$bctIDCM+Ethanoldiploid0.001d1G1 0.01657615 0.01500470 1.105
## my$bctIDCM+Ethanoldiploid0.001d1G2 -0.01127658 0.01818702 -0.620
## my$bctIDCM+Ethanoldiploid0.001d1H2 -0.01386016 0.01837215 -0.754
## my$bctIDCM+Ethanoldiploid0.001d1H4 0.00296372 0.02376768 0.125
## my$bctIDCM+Ethanoldiploid0.001d1H5 0.00347374 0.01381229 0.251
## my$bctIDCM+Ethanoldiploid0.001d1H6 0.00333766 0.01279047 0.261
## my$bctIDCM+Ethanoldiploid0.001d1H7 -0.00551949 0.02558074 -0.216
## my$bctIDCM+Saltdiploid0.001d1A2 0.04403600 0.01205900 3.652
## my$bctIDCM+Saltdiploid0.001d1A3 0.01686781 0.01180626 1.429
## my$bctIDCM+Saltdiploid0.001d1A4 0.02820342 0.01240104 2.274
## my$bctIDCM+Saltdiploid0.001d1A5 0.39852910 0.19667177 2.026
## my$bctIDCM+Saltdiploid0.001d1A7 0.02578701 0.01322947 1.949
## my$bctIDCM+Saltdiploid0.001d1F3 0.14079891 0.02033980 6.922
## my$bctIDCM+Saltdiploid0.001d1F4 0.18806044 0.02296431 8.189
## my$bctIDCM+Saltdiploid0.001d1F5 0.19913858 0.15799739 1.260
## my$bctIDCM+Saltdiploid0.001d1F6 0.23451078 0.01480773 15.837
## my$bctIDCM+Saltdiploid0.001d1F7 0.15255139 0.02121079 7.192
## my$bctIDCM+Saltdiploid0.001d1F8 0.12888704 0.01760927 7.319
## my$bctIDCM+Saltdiploid0.001d1G3 0.10101471 0.03918185 2.578
## my$bctIDCM+Saltdiploid0.001d1G4 0.13402619 0.04517263 2.967
## my$bctIDCM+Saltdiploid0.001d1G5 0.09585476 0.01936410 4.950
## my$bctIDCM+Saltdiploid0.001d1G6 0.08971402 0.03285475 2.731
## my$bctIDCM+Saltdiploid0.001d1G7 0.08740043 0.02106254 4.150
## my$bctIDCM+Saltdiploid0.001d1G8 0.12572385 0.03290001 3.821
## my$bctIDCM+Saltdiploid0.001d1H2 0.00495816 0.01539561 0.322
## my$bctIDCM+Saltdiploid0.001d1H3 0.02115633 0.01617586 1.308
## my$bctIDCM+Saltdiploid0.001d1H4 0.02137396 0.01932366 1.106
## my$bctIDCM+Saltdiploid0.001d1H5 0.00354951 0.01200179 0.296
## my$bctIDCM+Saltdiploid0.001d1H7 0.09417670 0.02624046 3.589
## my$bctIDCMdiploid0.00025d1A2 0.01559982 0.01599335 0.975
## my$bctIDCMdiploid0.00025d1A3 -0.00878718 0.01495776 -0.587
## my$bctIDCMdiploid0.00025d1A4 0.00174608 0.01441827 0.121
## my$bctIDCMdiploid0.00025d1A5 0.02677688 0.01511949 1.771
## my$bctIDCMdiploid0.00025d1A6 0.01198957 0.01303287 0.920
## my$bctIDCMdiploid0.00025d1A7 -0.00440271 0.01621492 -0.272
## my$bctIDCMdiploid0.00025d1B10 -0.00809898 0.01653299 -0.490
## my$bctIDCMdiploid0.00025d1B11 0.01723177 0.01242047 1.387
## my$bctIDCMdiploid0.00025d1B12 0.02983743 0.01830367 1.630
## my$bctIDCMdiploid0.00025d1B9 0.02155039 0.01336058 1.613
## my$bctIDCMdiploid0.00025d1C10 -0.01674094 0.01859239 -0.900
## my$bctIDCMdiploid0.00025d1C11 0.03216629 0.01622343 1.983
## my$bctIDCMdiploid0.00025d1C12 0.01496869 0.01326625 1.128
## my$bctIDCMdiploid0.00025d1C9 -0.00518047 0.01188006 -0.436
## my$bctIDCMdiploid0.00025d1D2 0.00370897 0.05550949 0.067
## my$bctIDCMdiploid0.00025d1E2 0.01289359 0.01052145 1.225
## my$bctIDCMdiploid0.00025d1H2 0.00760594 0.01762321 0.432
## my$bctIDCMdiploid0.00025d1H3 -0.00458899 0.01745253 -0.263
## my$bctIDCMdiploid0.00025d1H4 0.01843903 0.02426240 0.760
## my$bctIDCMdiploid0.00025d1H5 0.02541993 0.01377634 1.845
## my$bctIDCMdiploid0.00025d1H6 0.00414724 0.01352400 0.307
## my$bctIDCMdiploid0.00025d1H7 0.00190764 0.02782539 0.069
## my$bctIDCMdiploid0.001d1A11 0.03612225 0.01815435 1.990
## my$bctIDCMdiploid0.001d1A2 0.00809289 0.01417709 0.571
## my$bctIDCMdiploid0.001d1A2B 0.00927271 0.01214216 0.764
## my$bctIDCMdiploid0.001d1A3 0.00032174 0.01485237 0.022
## my$bctIDCMdiploid0.001d1A3B 0.00398055 0.01385717 0.287
## my$bctIDCMdiploid0.001d1A4 0.01780845 0.01310805 1.359
## my$bctIDCMdiploid0.001d1A4B 0.00153330 0.01338588 0.115
## my$bctIDCMdiploid0.001d1A5 0.02937872 0.01569201 1.872
## my$bctIDCMdiploid0.001d1A5B -0.00512256 0.01465871 -0.349
## my$bctIDCMdiploid0.001d1A6 0.02327336 0.01302785 1.786
## my$bctIDCMdiploid0.001d1A6B 0.00955064 0.01339667 0.713
## my$bctIDCMdiploid0.001d1A7 0.04290593 0.01344789 3.191
## my$bctIDCMdiploid0.001d1A8 0.01512575 0.01169949 1.293
## my$bctIDCMdiploid0.001d1A9 0.02268337 0.01128163 2.011
## my$bctIDCMdiploid0.001d1B1 0.05232713 0.01916650 2.730
## my$bctIDCMdiploid0.001d1B2 0.04063255 0.01363624 2.980
## my$bctIDCMdiploid0.001d1C1 0.05496896 0.01813523 3.031
## my$bctIDCMdiploid0.001d1C2 0.03217244 0.01299148 2.476
## my$bctIDCMdiploid0.001d1D4 0.08813997 0.05292837 1.665
## my$bctIDCMdiploid0.001d1D5 -0.01879019 0.01310839 -1.433
## my$bctIDCMdiploid0.001d1D6 0.04266468 0.04243456 1.005
## my$bctIDCMdiploid0.001d1D7 0.01221091 0.01202676 1.015
## my$bctIDCMdiploid0.001d1D8 -0.01270792 0.01081313 -1.175
## my$bctIDCMdiploid0.001d1E4 0.00301728 0.00782870 0.385
## my$bctIDCMdiploid0.001d1E5 0.03661495 0.02836404 1.291
## my$bctIDCMdiploid0.001d1E6 -0.02094510 0.00919326 -2.278
## my$bctIDCMdiploid0.001d1E7 0.02727065 0.01036588 2.631
## my$bctIDCMdiploid0.001d1E8 -0.00435115 0.01093102 -0.398
## my$bctIDCMdiploid0.001d1H11 0.03103102 0.01491899 2.080
## my$bctIDCMdiploid0.001d1H2 0.01207171 0.01797336 0.672
## my$bctIDCMdiploid0.001d1H2B -0.01771525 0.01722331 -1.029
## my$bctIDCMdiploid0.001d1H3 -0.00572467 0.01932551 -0.296
## my$bctIDCMdiploid0.001d1H3B 0.00004411 0.01824231 0.002
## my$bctIDCMdiploid0.001d1H4 0.00887392 0.02490203 0.356
## my$bctIDCMdiploid0.001d1H4B -0.00268735 0.02541084 -0.106
## my$bctIDCMdiploid0.001d1H5 0.01359003 0.01528546 0.889
## my$bctIDCMdiploid0.001d1H5B -0.00274906 0.01359686 -0.202
## my$bctIDCMdiploid0.001d1H6 0.03458717 0.01367055 2.530
## my$bctIDCMdiploid0.001d1H6B 0.02557412 0.01329414 1.924
## my$bctIDCMdiploid0.001d1H7 0.00942620 0.02647801 0.356
## my$bctIDCMdiploid0.001d1H8 0.01372130 0.02208970 0.621
## my$bctIDCMdiploid0.001d1H9 0.01922550 0.02317816 0.829
## my$bctIDCMdiploid0.004d1A2 0.00073845 0.01697648 0.043
## my$bctIDCMdiploid0.004d1A3 0.01219203 0.01903325 0.641
## my$bctIDCMdiploid0.004d1A4 0.05056340 0.01731766 2.920
## my$bctIDCMdiploid0.004d1A5 0.05149485 0.01798080 2.864
## my$bctIDCMdiploid0.004d1A6 0.01931859 0.01722893 1.121
## my$bctIDCMdiploid0.004d1A7 0.02865780 0.02004130 1.430
## my$bctIDCMdiploid0.004d1B3 0.02462782 0.01445493 1.704
## my$bctIDCMdiploid0.004d1B4 0.03125331 0.01659619 1.883
## my$bctIDCMdiploid0.004d1B5 0.04298949 0.01367318 3.144
## my$bctIDCMdiploid0.004d1B6 0.05327518 0.01738231 3.065
## my$bctIDCMdiploid0.004d1B7 0.07749931 0.01526527 5.077
## my$bctIDCMdiploid0.004d1C3 0.04273794 0.01469507 2.908
## my$bctIDCMdiploid0.004d1C4 0.02312320 0.01388803 1.665
## my$bctIDCMdiploid0.004d1C5 0.04334705 0.01293091 3.352
## my$bctIDCMdiploid0.004d1C6 0.06362223 0.01397915 4.551
## my$bctIDCMdiploid0.004d1C7 0.03835768 0.02896918 1.324
## my$bctIDCMdiploid0.004d1H2 0.02225147 0.02308198 0.964
## my$bctIDCMdiploid0.004d1H3 0.02106572 0.02173722 0.969
## my$bctIDCMdiploid0.004d1H4 0.00548235 0.03044485 0.180
## my$bctIDCMdiploid0.004d1H5 0.06026645 0.01911119 3.153
## my$bctIDCMdiploid0.004d1H6 0.04022208 0.01766259 2.277
## my$bctIDCMdiploid0.004d1H7 0.01164662 0.03155608 0.369
## my$bctIDCMhaploid0.001d1A2 0.17748276 0.18501465 0.959
## my$bctIDCMhaploid0.001d1A3 0.03475922 0.01642162 2.117
## my$bctIDCMhaploid0.001d1A3B 0.02653209 0.01137336 2.333
## my$bctIDCMhaploid0.001d1A4 0.07888998 0.01157424 6.816
## my$bctIDCMhaploid0.001d1A4B 0.00800659 0.01199416 0.668
## my$bctIDCMhaploid0.001d1A5 0.07567154 0.01371413 5.518
## my$bctIDCMhaploid0.001d1A5B 0.05376713 0.01259551 4.269
## my$bctIDCMhaploid0.001d1A6 0.02913551 0.01052784 2.767
## my$bctIDCMhaploid0.001d1A6B 0.02748147 0.00908708 3.024
## my$bctIDCMhaploid0.001d1A7 0.28424691 0.22971593 1.237
## my$bctIDCMhaploid0.001d1A7B 0.37549211 0.27832375 1.349
## my$bctIDCMhaploid0.001d1H2 0.05734718 0.01492184 3.843
## my$bctIDCMhaploid0.001d1H3 0.00588798 0.01683299 0.350
## my$bctIDCMhaploid0.001d1H3B 0.02271945 0.01846922 1.230
## my$bctIDCMhaploid0.001d1H4 0.06491278 0.03830520 1.695
## my$bctIDCMhaploid0.001d1H4B 0.01454690 0.01990335 0.731
## my$bctIDCMhaploid0.001d1H5 0.24925362 0.21028584 1.185
## my$bctIDCMhaploid0.001d1H5B 0.07768822 0.01659155 4.682
## my$bctIDCMhaploid0.001d1H6 0.10010351 0.02954796 3.388
## my$bctIDCMhaploid0.001d1H6B 0.11256605 0.02312772 4.867
## my$bctIDCMhaploid0.001d1H7 0.08865934 0.02084663 4.253
## my$bctIDCMhaploid0.001d1H7B 0.07939256 0.01932983 4.107
## Pr(>|t|)
## my$bctIDCM+Ethanoldiploid0.001d1A2 0.854419
## my$bctIDCM+Ethanoldiploid0.001d1A4 0.196832
## my$bctIDCM+Ethanoldiploid0.001d1A5 0.908990
## my$bctIDCM+Ethanoldiploid0.001d1A6 0.465352
## my$bctIDCM+Ethanoldiploid0.001d1A7 0.595903
## my$bctIDCM+Ethanoldiploid0.001d1D10 0.001570 **
## my$bctIDCM+Ethanoldiploid0.001d1D11 0.006650 **
## my$bctIDCM+Ethanoldiploid0.001d1D12 0.018906 *
## my$bctIDCM+Ethanoldiploid0.001d1D9 0.248287
## my$bctIDCM+Ethanoldiploid0.001d1E10 0.00002165288566661 ***
## my$bctIDCM+Ethanoldiploid0.001d1E11 0.005182 **
## my$bctIDCM+Ethanoldiploid0.001d1E12 0.012147 *
## my$bctIDCM+Ethanoldiploid0.001d1E9 0.350903
## my$bctIDCM+Ethanoldiploid0.001d1F1 0.286224
## my$bctIDCM+Ethanoldiploid0.001d1F2 0.084602 .
## my$bctIDCM+Ethanoldiploid0.001d1G1 0.269859
## my$bctIDCM+Ethanoldiploid0.001d1G2 0.535545
## my$bctIDCM+Ethanoldiploid0.001d1H2 0.450992
## my$bctIDCM+Ethanoldiploid0.001d1H4 0.900820
## my$bctIDCM+Ethanoldiploid0.001d1H5 0.801543
## my$bctIDCM+Ethanoldiploid0.001d1H6 0.794250
## my$bctIDCM+Ethanoldiploid0.001d1H7 0.829265
## my$bctIDCM+Saltdiploid0.001d1A2 0.000291 ***
## my$bctIDCM+Saltdiploid0.001d1A3 0.153770
## my$bctIDCM+Saltdiploid0.001d1A4 0.023413 *
## my$bctIDCM+Saltdiploid0.001d1A5 0.043309 *
## my$bctIDCM+Saltdiploid0.001d1A7 0.051883 .
## my$bctIDCM+Saltdiploid0.001d1F3 0.00000000001514729 ***
## my$bctIDCM+Saltdiploid0.001d1F4 0.00000000000000266 ***
## my$bctIDCM+Saltdiploid0.001d1F5 0.208173
## my$bctIDCM+Saltdiploid0.001d1F6 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F7 0.00000000000263293 ***
## my$bctIDCM+Saltdiploid0.001d1F8 0.00000000000113446 ***
## my$bctIDCM+Saltdiploid0.001d1G3 0.010247 *
## my$bctIDCM+Saltdiploid0.001d1G4 0.003166 **
## my$bctIDCM+Saltdiploid0.001d1G5 0.00000104573824082 ***
## my$bctIDCM+Saltdiploid0.001d1G6 0.006566 **
## my$bctIDCM+Saltdiploid0.001d1G7 0.00003974952106726 ***
## my$bctIDCM+Saltdiploid0.001d1G8 0.000151 ***
## my$bctIDCM+Saltdiploid0.001d1H2 0.747562
## my$bctIDCM+Saltdiploid0.001d1H3 0.191568
## my$bctIDCM+Saltdiploid0.001d1H4 0.269265
## my$bctIDCM+Saltdiploid0.001d1H5 0.767557
## my$bctIDCM+Saltdiploid0.001d1H7 0.000368 ***
## my$bctIDCMdiploid0.00025d1A2 0.329882
## my$bctIDCMdiploid0.00025d1A3 0.557181
## my$bctIDCMdiploid0.00025d1A4 0.903664
## my$bctIDCMdiploid0.00025d1A5 0.077226 .
## my$bctIDCMdiploid0.00025d1A6 0.358086
## my$bctIDCMdiploid0.00025d1A7 0.786112
## my$bctIDCMdiploid0.00025d1B10 0.624463
## my$bctIDCMdiploid0.00025d1B11 0.166007
## my$bctIDCMdiploid0.00025d1B12 0.103764
## my$bctIDCMdiploid0.00025d1B9 0.107440
## my$bctIDCMdiploid0.00025d1C10 0.368373
## my$bctIDCMdiploid0.00025d1C11 0.048000 *
## my$bctIDCMdiploid0.00025d1C12 0.259774
## my$bctIDCMdiploid0.00025d1C9 0.662997
## my$bctIDCMdiploid0.00025d1D2 0.946757
## my$bctIDCMdiploid0.00025d1E2 0.221035
## my$bctIDCMdiploid0.00025d1H2 0.666246
## my$bctIDCMdiploid0.00025d1H3 0.792714
## my$bctIDCMdiploid0.00025d1H4 0.447657
## my$bctIDCMdiploid0.00025d1H5 0.065659 .
## my$bctIDCMdiploid0.00025d1H6 0.759244
## my$bctIDCMdiploid0.00025d1H7 0.945372
## my$bctIDCMdiploid0.001d1A11 0.047218 *
## my$bctIDCMdiploid0.001d1A2 0.568387
## my$bctIDCMdiploid0.001d1A2B 0.445453
## my$bctIDCMdiploid0.001d1A3 0.982727
## my$bctIDCMdiploid0.001d1A3B 0.774047
## my$bctIDCMdiploid0.001d1A4 0.174949
## my$bctIDCMdiploid0.001d1A4B 0.908856
## my$bctIDCMdiploid0.001d1A5 0.061817 .
## my$bctIDCMdiploid0.001d1A5B 0.726909
## my$bctIDCMdiploid0.001d1A6 0.074694 .
## my$bctIDCMdiploid0.001d1A6B 0.476265
## my$bctIDCMdiploid0.001d1A7 0.001518 **
## my$bctIDCMdiploid0.001d1A8 0.196716
## my$bctIDCMdiploid0.001d1A9 0.044951 *
## my$bctIDCMdiploid0.001d1B1 0.006576 **
## my$bctIDCMdiploid0.001d1B2 0.003039 **
## my$bctIDCMdiploid0.001d1C1 0.002576 **
## my$bctIDCMdiploid0.001d1C2 0.013632 *
## my$bctIDCMdiploid0.001d1D4 0.096546 .
## my$bctIDCMdiploid0.001d1D5 0.152415
## my$bctIDCMdiploid0.001d1D6 0.315226
## my$bctIDCMdiploid0.001d1D7 0.310496
## my$bctIDCMdiploid0.001d1D8 0.240516
## my$bctIDCMdiploid0.001d1E4 0.700111
## my$bctIDCMdiploid0.001d1E5 0.197395
## my$bctIDCMdiploid0.001d1E6 0.023170 *
## my$bctIDCMdiploid0.001d1E7 0.008807 **
## my$bctIDCMdiploid0.001d1E8 0.690775
## my$bctIDCMdiploid0.001d1H11 0.038086 *
## my$bctIDCMdiploid0.001d1H2 0.502150
## my$bctIDCMdiploid0.001d1H2B 0.304231
## my$bctIDCMdiploid0.001d1H3 0.767194
## my$bctIDCMdiploid0.001d1H3B 0.998072
## my$bctIDCMdiploid0.001d1H4 0.721741
## my$bctIDCMdiploid0.001d1H4B 0.915822
## my$bctIDCMdiploid0.001d1H5 0.374428
## my$bctIDCMdiploid0.001d1H5B 0.839864
## my$bctIDCMdiploid0.001d1H6 0.011740 *
## my$bctIDCMdiploid0.001d1H6B 0.055013 .
## my$bctIDCMdiploid0.001d1H7 0.722004
## my$bctIDCMdiploid0.001d1H8 0.534803
## my$bctIDCMdiploid0.001d1H9 0.407275
## my$bctIDCMdiploid0.004d1A2 0.965323
## my$bctIDCMdiploid0.004d1A3 0.522127
## my$bctIDCMdiploid0.004d1A4 0.003677 **
## my$bctIDCMdiploid0.004d1A5 0.004378 **
## my$bctIDCMdiploid0.004d1A6 0.262755
## my$bctIDCMdiploid0.004d1A7 0.153420
## my$bctIDCMdiploid0.004d1B3 0.089106 .
## my$bctIDCMdiploid0.004d1B4 0.060315 .
## my$bctIDCMdiploid0.004d1B5 0.001775 **
## my$bctIDCMdiploid0.004d1B6 0.002307 **
## my$bctIDCMdiploid0.004d1B7 0.00000056011850773 ***
## my$bctIDCMdiploid0.004d1C3 0.003811 **
## my$bctIDCMdiploid0.004d1C4 0.096605 .
## my$bctIDCMdiploid0.004d1C5 0.000868 ***
## my$bctIDCMdiploid0.004d1C6 0.00000684943418923 ***
## my$bctIDCMdiploid0.004d1C7 0.186138
## my$bctIDCMdiploid0.004d1H2 0.335547
## my$bctIDCMdiploid0.004d1H3 0.333005
## my$bctIDCMdiploid0.004d1H4 0.857174
## my$bctIDCMdiploid0.004d1H5 0.001720 **
## my$bctIDCMdiploid0.004d1H6 0.023234 *
## my$bctIDCMdiploid0.004d1H7 0.712242
## my$bctIDCMhaploid0.001d1A2 0.337921
## my$bctIDCMhaploid0.001d1A3 0.034829 *
## my$bctIDCMhaploid0.001d1A3B 0.020091 *
## my$bctIDCMhaploid0.001d1A4 0.00000000002976149 ***
## my$bctIDCMhaploid0.001d1A4B 0.504764
## my$bctIDCMhaploid0.001d1A5 0.00000005766073013 ***
## my$bctIDCMhaploid0.001d1A5B 0.00002392620167394 ***
## my$bctIDCMhaploid0.001d1A6 0.005879 **
## my$bctIDCMhaploid0.001d1A6B 0.002633 **
## my$bctIDCMhaploid0.001d1A7 0.216581
## my$bctIDCMhaploid0.001d1A7B 0.177968
## my$bctIDCMhaploid0.001d1H2 0.000139 ***
## my$bctIDCMhaploid0.001d1H3 0.726659
## my$bctIDCMhaploid0.001d1H3B 0.219285
## my$bctIDCMhaploid0.001d1H4 0.090830 .
## my$bctIDCMhaploid0.001d1H4B 0.465230
## my$bctIDCMhaploid0.001d1H5 0.236513
## my$bctIDCMhaploid0.001d1H5B 0.00000374585588773 ***
## my$bctIDCMhaploid0.001d1H6 0.000766 ***
## my$bctIDCMhaploid0.001d1H6B 0.00000156280464386 ***
## my$bctIDCMhaploid0.001d1H7 0.00002561158235811 ***
## my$bctIDCMhaploid0.001d1H7B 0.00004746151175424 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.922 on 456 degrees of freedom
## Multiple R-squared: 0.7186, Adjusted R-squared: 0.6248
## F-statistic: 7.66 on 152 and 456 DF, p-value: < 0.00000000000000022
myrmse <- sqrt(mean(residuals(mymod)^2)) * 100 # this is the rmse from the fitness equivalence model*100 --> effect size of 1 corresonds to an ~4.307 fitness dif
myeffectsize <- 1/myrmse # effect size of 1 is a 4.307 fitnesss change, so for a fitness change of 1.00, expect effect size of 1/myrmse
# power to detect treatment effects ------------------------------
myv <- (22 * 2) - 1 - 1
mypowersize <- (pwr.f2.test(u = 1, v = myv, f2 = myeffectsize, sig.level = 0.05))$power # powersize for plotting below, where 22 is the number of BCs in any given treatment.
# prepare plot data ----------------------------------------
mypower <- matrix(nrow = 100, ncol = 5)
colnames(mypower) <- c("u", "v", "power", "f2", "sig.level")
mypower <- as.data.frame(mypower)
mypower$u <- 1
mypower$v <- c(myv, myv * 0.9, myv * 0.8, myv * 0.7, myv * 0.6, myv * 0.5, myv *
0.4, myv * 0.3, myv * 0.2, myv * 0.1)
mypower$power <- NA
mypower$f2 <- c(rep(myeffectsize * 2, 10), rep(myeffectsize * 1.75, 10), rep(myeffectsize *
1.5, 10), rep(myeffectsize * 1.25, 10), rep(myeffectsize * 1, 10), rep(myeffectsize *
0.75, 10), rep(myeffectsize * 0.5, 10), rep(myeffectsize * 0.25, 10), rep(myeffectsize *
0.1, 10), rep(myeffectsize * 0.05, 10))
mypower$sig.level <- 0.05
mypower$group <- c(rep(1, 10), rep(2, 10), rep(3, 10), rep(4, 10), rep(5, 10),
rep(6, 10), rep(7, 10), rep(8, 10), rep(9, 10), rep(10, 10))
mypower$group <- as.factor(mypower$group)
for (i in 1:nrow(mypower)) {
pow <- pwr.f2.test(u = mypower$u[i], v = mypower$v[i], f2 = mypower$f2[i],
sig.level = mypower$sig.level[i])
mypower$power[i] <- pow$power
}
# model output
# -----------------------------------------------------------------
mypower2 <- round(mypower[, 1:5], digits = 5)
tab_df(mypower2, file = "000_Additional_File_10.html")| u | v | power | f2 | sig.level |
|---|---|---|---|---|
| 1 | 42 | 0.99294 | 0.46428 | 0.05 |
| 1 | 37.8 | 0.98703 | 0.46428 | 0.05 |
| 1 | 33.6 | 0.97653 | 0.46428 | 0.05 |
| 1 | 29.4 | 0.95825 | 0.46428 | 0.05 |
| 1 | 25.2 | 0.92721 | 0.46428 | 0.05 |
| 1 | 21 | 0.876 | 0.46428 | 0.05 |
| 1 | 16.8 | 0.79451 | 0.46428 | 0.05 |
| 1 | 12.6 | 0.67074 | 0.46428 | 0.05 |
| 1 | 8.4 | 0.49405 | 0.46428 | 0.05 |
| 1 | 4.2 | 0.26374 | 0.46428 | 0.05 |
| 1 | 42 | 0.98497 | 0.40625 | 0.05 |
| 1 | 37.8 | 0.97482 | 0.40625 | 0.05 |
| 1 | 33.6 | 0.95838 | 0.40625 | 0.05 |
| 1 | 29.4 | 0.93224 | 0.40625 | 0.05 |
| 1 | 25.2 | 0.8916 | 0.40625 | 0.05 |
| 1 | 21 | 0.83007 | 0.40625 | 0.05 |
| 1 | 16.8 | 0.7399 | 0.40625 | 0.05 |
| 1 | 12.6 | 0.61317 | 0.40625 | 0.05 |
| 1 | 8.4 | 0.44459 | 0.40625 | 0.05 |
| 1 | 4.2 | 0.23739 | 0.40625 | 0.05 |
| 1 | 42 | 0.96879 | 0.34821 | 0.05 |
| 1 | 37.8 | 0.95221 | 0.34821 | 0.05 |
| 1 | 33.6 | 0.92766 | 0.34821 | 0.05 |
| 1 | 29.4 | 0.89194 | 0.34821 | 0.05 |
| 1 | 25.2 | 0.84097 | 0.34821 | 0.05 |
| 1 | 21 | 0.76995 | 0.34821 | 0.05 |
| 1 | 16.8 | 0.67383 | 0.34821 | 0.05 |
| 1 | 12.6 | 0.5484 | 0.34821 | 0.05 |
| 1 | 8.4 | 0.39238 | 0.34821 | 0.05 |
| 1 | 4.2 | 0.21076 | 0.34821 | 0.05 |
| 1 | 42 | 0.93704 | 0.29018 | 0.05 |
| 1 | 37.8 | 0.91164 | 0.29018 | 0.05 |
| 1 | 33.6 | 0.87718 | 0.29018 | 0.05 |
| 1 | 29.4 | 0.83114 | 0.29018 | 0.05 |
| 1 | 25.2 | 0.77067 | 0.29018 | 0.05 |
| 1 | 21 | 0.69287 | 0.29018 | 0.05 |
| 1 | 16.8 | 0.59524 | 0.29018 | 0.05 |
| 1 | 12.6 | 0.4765 | 0.29018 | 0.05 |
| 1 | 8.4 | 0.33773 | 0.29018 | 0.05 |
| 1 | 4.2 | 0.18392 | 0.29018 | 0.05 |
| 1 | 42 | 0.87741 | 0.23214 | 0.05 |
| 1 | 37.8 | 0.84173 | 0.23214 | 0.05 |
| 1 | 33.6 | 0.79723 | 0.23214 | 0.05 |
| 1 | 29.4 | 0.74243 | 0.23214 | 0.05 |
| 1 | 25.2 | 0.6759 | 0.23214 | 0.05 |
| 1 | 21 | 0.59651 | 0.23214 | 0.05 |
| 1 | 16.8 | 0.50371 | 0.23214 | 0.05 |
| 1 | 12.6 | 0.39793 | 0.23214 | 0.05 |
| 1 | 8.4 | 0.28108 | 0.23214 | 0.05 |
| 1 | 4.2 | 0.15695 | 0.23214 | 0.05 |
| 1 | 42 | 0.77158 | 0.17411 | 0.05 |
| 1 | 37.8 | 0.72743 | 0.17411 | 0.05 |
| 1 | 33.6 | 0.67653 | 0.17411 | 0.05 |
| 1 | 29.4 | 0.61844 | 0.17411 | 0.05 |
| 1 | 25.2 | 0.55288 | 0.17411 | 0.05 |
| 1 | 21 | 0.47987 | 0.17411 | 0.05 |
| 1 | 16.8 | 0.39983 | 0.17411 | 0.05 |
| 1 | 12.6 | 0.3137 | 0.17411 | 0.05 |
| 1 | 8.4 | 0.22305 | 0.17411 | 0.05 |
| 1 | 4.2 | 0.12996 | 0.17411 | 0.05 |
| 1 | 42 | 0.59797 | 0.11607 | 0.05 |
| 1 | 37.8 | 0.55355 | 0.11607 | 0.05 |
| 1 | 33.6 | 0.50585 | 0.11607 | 0.05 |
| 1 | 29.4 | 0.45496 | 0.11607 | 0.05 |
| 1 | 25.2 | 0.40109 | 0.11607 | 0.05 |
| 1 | 21 | 0.34458 | 0.11607 | 0.05 |
| 1 | 16.8 | 0.28588 | 0.11607 | 0.05 |
| 1 | 12.6 | 0.2256 | 0.11607 | 0.05 |
| 1 | 8.4 | 0.16446 | 0.11607 | 0.05 |
| 1 | 4.2 | 0.10305 | 0.11607 | 0.05 |
| 1 | 42 | 0.34535 | 0.05804 | 0.05 |
| 1 | 37.8 | 0.31637 | 0.05804 | 0.05 |
| 1 | 33.6 | 0.28693 | 0.05804 | 0.05 |
| 1 | 29.4 | 0.25712 | 0.05804 | 0.05 |
| 1 | 25.2 | 0.22705 | 0.05804 | 0.05 |
| 1 | 21 | 0.19683 | 0.05804 | 0.05 |
| 1 | 16.8 | 0.16657 | 0.05804 | 0.05 |
| 1 | 12.6 | 0.13638 | 0.05804 | 0.05 |
| 1 | 8.4 | 0.10634 | 0.05804 | 0.05 |
| 1 | 4.2 | 0.07635 | 0.05804 | 0.05 |
| 1 | 42 | 0.16705 | 0.02321 | 0.05 |
| 1 | 37.8 | 0.15503 | 0.02321 | 0.05 |
| 1 | 33.6 | 0.14305 | 0.02321 | 0.05 |
| 1 | 29.4 | 0.1311 | 0.02321 | 0.05 |
| 1 | 25.2 | 0.11921 | 0.02321 | 0.05 |
| 1 | 21 | 0.10737 | 0.02321 | 0.05 |
| 1 | 16.8 | 0.0956 | 0.02321 | 0.05 |
| 1 | 12.6 | 0.0839 | 0.02321 | 0.05 |
| 1 | 8.4 | 0.07223 | 0.02321 | 0.05 |
| 1 | 4.2 | 0.06049 | 0.02321 | 0.05 |
| 1 | 42 | 0.10748 | 0.01161 | 0.05 |
| 1 | 37.8 | 0.10161 | 0.01161 | 0.05 |
| 1 | 33.6 | 0.09576 | 0.01161 | 0.05 |
| 1 | 29.4 | 0.08993 | 0.01161 | 0.05 |
| 1 | 25.2 | 0.08412 | 0.01161 | 0.05 |
| 1 | 21 | 0.07833 | 0.01161 | 0.05 |
| 1 | 16.8 | 0.07256 | 0.01161 | 0.05 |
| 1 | 12.6 | 0.06681 | 0.01161 | 0.05 |
| 1 | 8.4 | 0.06105 | 0.01161 | 0.05 |
| 1 | 4.2 | 0.05524 | 0.01161 | 0.05 |
# -----------------------------------------------------------------------------
myheight <- 7
mywidth <- 7
mypower$n <- mypower$v + 2
mypower$n <- factor(mypower$n, levels = c(myv + 2, (myv * 0.9) + 2, (myv * 0.8) +
2, (myv * 0.7) + 2, (myv * 0.6) + 2, (myv * 0.5) + 2, (myv * 0.4) + 2, (myv *
0.3) + 2, (myv * 0.2) + 2, (myv * 0.1) + 2))
mypower$v <- factor(mypower$v, levels = c(myv, myv * 0.9, myv * 0.8, myv * 0.7,
myv * 0.6, myv * 0.5, myv * 0.4, myv * 0.3, myv * 0.2, myv * 0.1))
p <- ggplot(mypower, aes(x = f2, y = power, group = n)) + geom_vline(xintercept = myeffectsize,
linetype = "dashed", color = "darkgray") + geom_line(aes(color = n)) + geom_point(aes(color = n)) +
geom_point(x = myeffectsize, y = mypowersize, pch = 1, size = 5, color = "darkgray") +
theme_classic() + labs(tag = "D") + labs(color = "n (total barcodes \n in two treatments))") +
scale_x_continuous(name = "Fitness Difference between Treatments", breaks = c(myeffectsize *
0.05, myeffectsize * 0.1, myeffectsize * 0.25, myeffectsize * 0.5, myeffectsize *
0.75, myeffectsize, myeffectsize * 1.25, myeffectsize * 1.5, myeffectsize *
1.75, myeffectsize * 2), labels = c("0.05%", "", "0.25%", "0.5%", "0.75%",
"1.0%", "1.25%", "1.5%", "1.75%", "2.0%")) + theme(legend.direction = "horizontal") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
legend2 <- get_legend(p)
p <- p + theme(legend.position = "none")
p
pD <- p
# Plot using grid and output
# ---------------------------------------------------
mywidth <- 6.69
myheight <- 6.69
pdf(file = "000_Figure_3.pdf", width = mywidth, height = myheight)
grid.arrange(grid::textGrob("POC Fitness \n Assays", gp = grid::gpar(fontsize = 12,
fontface = "bold"), rot = 90), grid::textGrob("250-Generation \n Experiment",
gp = grid::gpar(fontsize = 12, fontface = "bold"), rot = 90), pA, pB, pC,
pD, legend1, legend2, layout_matrix = rbind(c(1, 3, 3, 3, 3, 4, 4, 4, 4,
4, 4), c(1, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4), c(1, 3, 3, 3, 3, 4, 4, 4,
4, 4, 4), c(1, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4), c(2, 5, 5, 5, 5, 6, 6,
6, 6, 6, 6), c(2, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6), c(2, 5, 5, 5, 5, 6,
6, 6, 6, 6, 6), c(2, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6), c(7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7), c(8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8)))
## Warning: Removed 18 rows containing missing values (geom_path).
## Warning: Removed 18 rows containing missing values (geom_point).
## Warning: Removed 18 rows containing missing values (geom_path).
## Warning: Removed 18 rows containing missing values (geom_point).
dev.off() # one column wide, equal height
## quartz_off_screen
## 2
# ------------------------------------------------------------------------------
rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7, my8, my9, my10,
mymod, mypower, mypower2, p, pA, pB, pC, pD, pow, i, myeffectsize, myheight,
mypowersize, myrmse, myv, mywidth, psd)Analysis: Assess effects of evoluitonary treatment on change in fitness in 250-generations of experimental evolution.
my <- myfa[order(myfa$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ----------------------------------------------------
mymod <- lmer(I(scale(deltafit)) ~ I(scale(ccdeltafit)) + treatment + (1 | bctID),
weights = rdeltafit, data = my, control = lmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 20000)))
# model output
# -----------------------------------------------------------------=
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
show.r2 = F, file = "000_Additional_File_13.html")| Â | I(scale(deltafit)) | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| (Intercept) | -0.31415 | -0.43012 – -0.19819 | -5.30957 | 0.00000047 |
| I(scale(ccdeltafit)) | -0.27053 | -0.30697 – -0.23408 | -14.54794 | <0.001 |
| CM+Ethanoldiploid 0.001 | -0.29452 | -0.49136 – -0.09767 | -2.93240 | 0.00398690 |
| CM+Saltdiploid 0.001 | 0.96163 | 0.75021 – 1.17306 | 8.91470 | <0.001 |
| C Mdiploid 0.00025 | -0.08752 | -0.28584 – 0.11081 | -0.86489 | 0.38870062 |
| C Mdiploid 0.004 | 0.34621 | 0.14533 – 0.54710 | 3.37792 | 0.00094905 |
| C Mhaploid 0.001 | 0.27966 | 0.06625 – 0.49307 | 2.56846 | 0.01130558 |
| Random Effects | ||||
| σ2 | 367.71 | |||
| Ï„00 bctID | 0.12 | |||
| ICC | 0.00 | |||
| N bctID | 152 | |||
| Observations | 608 | |||
# -----------------------------------------------------------------------------
my <- myfa_w[order(myfa_w$bctID), ] # placeholder dataframe
my <- my[my$sedeltafit < 0.029, ]
min(my$mndeltafit)
## [1] -0.03049231
max(my$mndeltafit)
## [1] 0.2345108
myx <- my$treatment
myy <- my$mndeltafit
myr <- my$mnrdeltafit
myc <- my$treatment
mytitle <- NULL
myylab <- "Fitness change"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myfillapha <- 0.35
myptalpha <- 0.35
mylinealpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.345
mywidth <- 6.69
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
geom_hline(yintercept = 0, lty = "dotted") + coord_flip() + theme_classic() +
theme(legend.position = "none") + ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) +
scale_x_discrete(labels = c("CM \n Diploid \n 1:1000", "CM+EtOH \n Diploid \n 1:1000",
"CM+NaCl \n Diploid \n 1:1000", "CM \n Diploid \n 1:4000", "CM \n Diploid \n 1:250",
"CM \n Haploid \n 1:1000")) + scale_y_continuous(breaks = seq(-0.05,
0.25, by = 0.05), labels = c("-0.05", "0", "0.05", "0.1", "0.15", "0.2",
"0.25"), limits = c(-0.05, 0.25)) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0)) + annotate("text",
x = c(2, 3, 4, 5, 6), y = c(0.065, 0.25, 0.045, 0.095, 0.115), label = c("**",
"***", "", "***", "*"), size = 8, fontface = "bold")
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Figure_5.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, weights)
rm(p, myc, myfill, myfillapha, myheight, mylinealpha, myoutline, mypalette,
myptalpha, myptsize, myr, mytextsize, mytitle, myx, myxlab, mywidth, myy,
myylab)Analysis: Calculate fixation rate summaries by treatment for the 250-generation experiment.
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# Review Results
# --------------------------------------------------------------
paste0("proportion of wells that attained fixation (n=76 total wells)= ", nrow(my[my$fixed ==
T, ])/nrow(my)) # prop fixed
## [1] "proportion of wells that attained fixation (n=76 total wells)= 0.118421052631579"
# CM diploid 1:1000
myt <- my[my$treatment == "CMdiploid0.001", ]
paste0("n wells in CMdiploid0.001 = ", nrow(myt)) # wells
## [1] "n wells in CMdiploid0.001 = 21"
paste0("number of fixed wells in treatment = ", nrow(myt[myt$fixed, ])) # number fixed
## [1] "number of fixed wells in treatment = 1"
paste0("proportion of fixed wells in treatment = ", nrow(myt[myt$fixed, ])/nrow(myt)) # prop fixed
## [1] "proportion of fixed wells in treatment = 0.0476190476190476"
# CM haploid 1:1000
myt <- my[my$treatment == "CMhaploid0.001", ]
paste0("n wells in CMhaploid0.001 = ", nrow(myt)) # wells
## [1] "n wells in CMhaploid0.001 = 11"
paste0("number of fixed wells in treatment = ", nrow(myt[myt$fixed, ])) # number fixed
## [1] "number of fixed wells in treatment = 5"
paste0("proportion of fixed wells in treatment = ", nrow(myt[myt$fixed, ])/nrow(myt)) # prop fixed
## [1] "proportion of fixed wells in treatment = 0.454545454545455"
# CM+Salt diploid 1:1000
myt <- my[my$treatment == "CM+Saltdiploid0.001", ]
paste0("n wells in CM+Saltdiploid0.001 = ", nrow(myt)) # wells
## [1] "n wells in CM+Saltdiploid0.001 = 11"
paste0("number of fixed wells in treatment = ", nrow(myt[myt$fixed, ])) # number fixed
## [1] "number of fixed wells in treatment = 3"
paste0("proportion of fixed wells in treatment = ", nrow(myt[myt$fixed, ])/nrow(myt)) # prop fixed
## [1] "proportion of fixed wells in treatment = 0.272727272727273"
# CM+Ethanol 1:1000
myt <- my[my$treatment == "CM+Ethanoldiploid0.001", ]
paste0("n wells in CM+Ethanoldiploid0.001 = ", nrow(myt)) # wells
## [1] "n wells in CM+Ethanoldiploid0.001 = 11"
paste0("number of fixed wells in treatment = ", nrow(myt[myt$fixed, ])) # number fixed
## [1] "number of fixed wells in treatment = 0"
paste0("proportion of fixed wells in treatment = ", nrow(myt[myt$fixed, ])/nrow(myt)) # prop fixed
## [1] "proportion of fixed wells in treatment = 0"
# CM diploid 1:250
myt <- my[my$treatment == "CMdiploid0.004", ]
paste0("n wells in CMdiploid0.004 = ", nrow(myt)) # wells
## [1] "n wells in CMdiploid0.004 = 11"
paste0("number of fixed wells in treatment = ", nrow(myt[myt$fixed, ])) # number fixed
## [1] "number of fixed wells in treatment = 0"
paste0("proportion of fixed wells in treatment = ", nrow(myt[myt$fixed, ])/nrow(myt)) # prop fixed
## [1] "proportion of fixed wells in treatment = 0"
# CM diploid 1:4000
myt <- my[my$treatment == "CMdiploid0.00025", ]
paste0("n wells in CMdiploid0.00025 = ", nrow(myt)) # wells
## [1] "n wells in CMdiploid0.00025 = 11"
paste0("number of fixed wells in treatment = ", nrow(myt[myt$fixed, ])) # number fixed
## [1] "number of fixed wells in treatment = 0"
paste0("proportion of fixed wells in treatment = ", nrow(myt[myt$fixed, ])/nrow(myt)) # prop fixed
## [1] "proportion of fixed wells in treatment = 0"
# -----------------------------------------------------------------------------
rm(my, myt)Analysis: Assess effects of evolutionary treatment on T-MAX (generation of maximum deviation from generation-0 barcoded strain abundance)
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ---------------------------------------------------- with initial
# proportions included in the model: mymod <- glm(my$tmaxdev ~ my$treatment
# + my$ccmaxdev_t0 + my$diff0, weights = my$rmaxdev_rt0); summary(mymod) #
# drop c as least significant term mymod <- glm(my$tmaxdev ~ my$treatment +
# my$diff0, weights = my$rmaxdev_rt0); summary(mymod) # diff 0 still not
# significant...run the model set without it.
# without: mymod <- glm(my$tmaxdev ~ my$treatment + my$ccmaxdev, weights =
# my$rmaxdev); summary(mymod) # drop c as least significant term
mymod <- lm(my$tmaxdev ~ my$treatment, weights = my$rmaxdev)
summary(mymod) # FINAL MODEL!
##
## Call:
## lm(formula = my$tmaxdev ~ my$treatment, weights = my$rmaxdev)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -45513 -3616 1146 3910 16785
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 233.5992 14.7420 15.846
## my$treatmentCM+Ethanoldiploid0.001 -20.8306 17.1490 -1.215
## my$treatmentCM+Saltdiploid0.001 -48.7017 18.3948 -2.648
## my$treatmentCMdiploid0.00025 2.2063 20.8325 0.106
## my$treatmentCMdiploid0.004 -43.3530 27.8363 -1.557
## my$treatmentCMhaploid0.001 0.8156 16.9681 0.048
## Pr(>|t|)
## (Intercept) <0.0000000000000002 ***
## my$treatmentCM+Ethanoldiploid0.001 0.229
## my$treatmentCM+Saltdiploid0.001 0.010 *
## my$treatmentCMdiploid0.00025 0.916
## my$treatmentCMdiploid0.004 0.124
## my$treatmentCMhaploid0.001 0.962
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9517 on 70 degrees of freedom
## Multiple R-squared: 0.1972, Adjusted R-squared: 0.1398
## F-statistic: 3.438 on 5 and 70 DF, p-value: 0.007779
anova(mymod) # test term significance with an lm hist(residuals(mymod), breaks = 25)
hist(expand_residuals(mymod, my$rmaxdev), breaks = 25) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here. 
plot(mymod) # check model fit 
summary(mymod)$r.squared # for R2 value
## [1] 0.19716
summary(mymod)$adj.r.squared # for adjusted R2 value
## [1] 0.1398143
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_14.html")| Â | my$tmaxdev | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| (Intercept) | 233.59916 | 204.70539 – 262.49293 | 15.84584 | <0.001 |
| CM+Ethanoldiploid 0.001 | -20.83060 | -54.44209 – 12.78089 | -1.21468 | 0.22856965 |
| CM+Saltdiploid 0.001 | -48.70167 | -84.75481 – -12.64854 | -2.64758 | 0.01000874 |
| C Mdiploid 0.00025 | 2.20631 | -38.62458 – 43.03720 | 0.10591 | 0.91595897 |
| C Mdiploid 0.004 | -43.35303 | -97.91111 – 11.20506 | -1.55743 | 0.12387850 |
| C Mhaploid 0.001 | 0.81564 | -32.44114 – 34.07241 | 0.04807 | 0.96179819 |
| Observations | 76 | |||
| R2 / R2 adjusted | 0.197 / 0.140 | |||
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- my$treatment
myy <- my$tmaxdev
myr <- my$rmaxdev
myc <- my$treatment
mytitle <- NULL
myylab <- "T-MAX (Generation)"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myptalpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
# geom_hline(yintercept = 0, lty = 'dotted') +
coord_flip() + theme_classic() + theme(legend.position = "none") + ggtitle(mytitle) +
ylab(myylab) + xlab(myxlab) + scale_x_discrete(labels = c("CM \n Diploid \n 0.001",
"CM+EtOH \n Diploid \n 0.001", "CM+NaCl \n Diploid \n 0.001", "CM \n Diploid \n 0.00025",
"CM \n Diploid \n 0.004", "CM \n Haploid \n 0.001")) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0)) + annotate("text",
x = c(3), y = c(255), label = "*", size = 8, fontface = "bold")
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_15.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, p, myc, myfill, myheight, myoutline, mypalette, myptalpha, myptsize,
myr, mytextsize, mytitle, mywidth, myx, myxlab, myy, myylab)Analysis: Assess effects of evolutionary treatment on M-MAX (magnitude of maximum deviation from generation-0 barcoded strain abundance)
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ---------------------------------------------------- with initial
# proportions included in the model: mymod <- glm(my$mmaxdev ~ my$treatment
# + my$ccmaxdev_t0 + my$diff0, weights = my$rmaxdev_rt0); summary(mymod) #
# drop c as least significant term mymod <- glm(my$mmaxdev ~ my$treatment +
# my$diff0, weights = my$rmaxdev_rt0); summary(mymod) # diff 0 still not
# significant...run the model set without it.
# without: mymod <- glm(my$mmaxdev ~ my$treatment + my$ccmaxdev, weights =
# my$rmaxdev); summary(mymod) # drop c as least significant term
mymod <- lm(my$mmaxdev ~ my$treatment, weights = my$rmaxdev)
summary(mymod) # FINAL MODEL!
##
## Call:
## lm(formula = my$mmaxdev ~ my$treatment, weights = my$rmaxdev)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -80.362 -11.841 -1.923 18.199 121.427
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.22746 0.05437 4.184 0.0000820
## my$treatmentCM+Ethanoldiploid0.001 -0.06495 0.06325 -1.027 0.30799
## my$treatmentCM+Saltdiploid0.001 0.29240 0.06784 4.310 0.0000524
## my$treatmentCMdiploid0.00025 0.11117 0.07683 1.447 0.15240
## my$treatmentCMdiploid0.004 0.03904 0.10266 0.380 0.70491
## my$treatmentCMhaploid0.001 0.17791 0.06258 2.843 0.00585
##
## (Intercept) ***
## my$treatmentCM+Ethanoldiploid0.001
## my$treatmentCM+Saltdiploid0.001 ***
## my$treatmentCMdiploid0.00025
## my$treatmentCMdiploid0.004
## my$treatmentCMhaploid0.001 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 35.1 on 70 degrees of freedom
## Multiple R-squared: 0.4564, Adjusted R-squared: 0.4176
## F-statistic: 11.76 on 5 and 70 DF, p-value: 0.00000002882
anova(mymod) # test term significance with an lm hist(residuals(mymod), breaks = 25)
hist(expand_residuals(mymod, my$rmaxdev), breaks = 25) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here. 
plot(mymod) # check model fit 
summary(mymod)$r.squared # for R2 value
## [1] 0.4564297
summary(mymod)$adj.r.squared # for adjusted R2 value
## [1] 0.4176032
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_16.html")| Â | my$mmaxdev | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| (Intercept) | 0.22746 | 0.12089 – 0.33402 | 4.18350 | 0.00008197 |
| CM+Ethanoldiploid 0.001 | -0.06495 | -0.18891 – 0.05901 | -1.02693 | 0.30798766 |
| CM+Saltdiploid 0.001 | 0.29240 | 0.15943 – 0.42537 | 4.30998 | 0.00005236 |
| C Mdiploid 0.00025 | 0.11117 | -0.03942 – 0.26175 | 1.44685 | 0.15240115 |
| C Mdiploid 0.004 | 0.03904 | -0.16218 – 0.24025 | 0.38025 | 0.70491018 |
| C Mhaploid 0.001 | 0.17791 | 0.05526 – 0.30057 | 2.84299 | 0.00585276 |
| Observations | 76 | |||
| R2 / R2 adjusted | 0.456 / 0.418 | |||
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- my$treatment
myy <- my$mmaxdev
myr <- my$rmaxdev
myc <- my$treatment
mytitle <- NULL
myylab <- "M-MAX"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myfillapha <- 0.35
myptalpha <- 0.35
mylinealpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
geom_hline(yintercept = 0, lty = "dotted") + coord_flip() + theme_classic() +
theme(legend.position = "none") + ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) +
scale_x_discrete(labels = c("CM \n Diploid \n 0.001", "CM+EtOH \n Diploid \n 0.001",
"CM+NaCl \n Diploid \n 0.001", "CM \n Diploid \n 0.00025", "CM \n Diploid \n 0.004",
"CM \n Haploid \n 0.001")) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0)) + annotate("text",
x = c(3, 6), y = c(0.91, 0.765), label = c("***", "**"), size = 8, fontface = "bold")
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_17.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, p, myc, myfill, myheight, myoutline, mypalette, myptalpha, myptsize,
myr, mytextsize, mytitle, mywidth, myx, myxlab, myy, myylab, myfillapha,
mylinealpha)Analysis: Assess effects of evolutionary treatment on T-MAX-RATE (generation of maximum rate of change in barcoded strain abundance)
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ---------------------------------------------------- with initial
# proportions included in the model: mymod <- glm(my$tmaxrate ~ my$treatment
# + my$ccmaxrate_t0 + my$diff0, weights = my$rmaxrate_rt0); summary(mymod) #
# drop c as least significant term mymod <- glm(my$tmaxrate ~ my$treatment +
# my$diff0, weights = my$rmaxrate_rt0); summary(mymod) # diff 0 still not
# significant...run the model set without it.
# without: mymod <- glm(my$tmaxrate ~ my$treatment + my$ccmaxrate, weights =
# my$rmaxrate); summary(mymod) # drop c as least significant term
mymod <- lm(my$tmaxrate ~ my$treatment, weights = my$rmaxrate)
summary(mymod) # Final model!
##
## Call:
## lm(formula = my$tmaxrate ~ my$treatment, weights = my$rmaxrate)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -36500 -2877 985 3554 25478
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 235.251 7.709 30.518
## my$treatmentCM+Ethanoldiploid0.001 11.439 10.407 1.099
## my$treatmentCM+Saltdiploid0.001 -19.045 12.176 -1.564
## my$treatmentCMdiploid0.00025 9.910 17.741 0.559
## my$treatmentCMdiploid0.004 6.309 11.672 0.541
## my$treatmentCMhaploid0.001 -52.717 11.261 -4.681
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## my$treatmentCM+Ethanoldiploid0.001 0.275
## my$treatmentCM+Saltdiploid0.001 0.122
## my$treatmentCMdiploid0.00025 0.578
## my$treatmentCMdiploid0.004 0.591
## my$treatmentCMhaploid0.001 0.0000135 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9066 on 70 degrees of freedom
## Multiple R-squared: 0.3847, Adjusted R-squared: 0.3408
## F-statistic: 8.755 on 5 and 70 DF, p-value: 0.000001732
anova(mymod) # test term significance with an lm hist(residuals(mymod), breaks = 25)
hist(expand_residuals(mymod, my$rmaxrate), breaks = 25) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here. 
plot(mymod) # check model fit 
summary(mymod)$r.squared # for R2 value
## [1] 0.3847431
summary(mymod)$adj.r.squared # for adjusted R2 value
## [1] 0.3407962
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_18.html")| Â | my$tmaxrate | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| (Intercept) | 235.25147 | 220.14297 – 250.35998 | 30.51820 | <0.001 |
| CM+Ethanoldiploid 0.001 | 11.43864 | -8.95850 – 31.83578 | 1.09914 | 0.27547276 |
| CM+Saltdiploid 0.001 | -19.04522 | -42.90918 – 4.81874 | -1.56420 | 0.12228104 |
| C Mdiploid 0.00025 | 9.90963 | -24.86202 – 44.68129 | 0.55857 | 0.57823532 |
| C Mdiploid 0.004 | 6.30932 | -16.56811 – 29.18674 | 0.54053 | 0.59054432 |
| C Mhaploid 0.001 | -52.71656 | -74.78791 – -30.64520 | -4.68130 | 0.00001354 |
| Observations | 76 | |||
| R2 / R2 adjusted | 0.385 / 0.341 | |||
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- my$treatment
myy <- my$tmaxrate
myr <- my$rmaxrate
myc <- my$treatment
mytitle <- NULL
myylab <- "T-MAX-RATE"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myptalpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
# geom_hline(yintercept = 0, lty = 'dotted') +
coord_flip() + theme_classic() + theme(legend.position = "none") + ggtitle(mytitle) +
ylab(myylab) + xlab(myxlab) + scale_x_discrete(labels = c("CM \n Diploid \n 0.001",
"CM+EtOH \n Diploid \n 0.001", "CM+NaCl \n Diploid \n 0.001", "CM \n Diploid \n 0.00025",
"CM \n Diploid \n 0.004", "CM \n Haploid \n 0.001")) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0)) + annotate("text",
x = c(6), y = c(257), label = "***", size = 8, fontface = "bold")
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_19.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, p, myc, myfill, myheight, myoutline, mypalette, myptalpha, myptsize,
myr, mytextsize, mytitle, mywidth, myx, myxlab, myy, myylab)Analysis: Assess effects of evolutionary treatment on M-MAX-RATE (magnitude of maximum rate of change in barcoded strain abundance)
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ---------------------------------------------------- with initial
# proportions included in the model: mymod <- glm(my$mmaxrate ~ my$treatment
# + my$ccmaxrate_t0 + my$diff0, weights = my$rmaxrate_rt0); summary(mymod) #
# drop c as least significant term mymod <- glm(my$mmaxrate ~ my$treatment +
# my$diff0, weights = my$rmaxrate_rt0); summary(mymod) # diff 0 still not
# significant...run the model set without it.
# without: mymod <- glm(my$mmaxrate ~ my$treatment + my$ccmaxrate, weights =
# my$rmaxrate); summary(mymod) # drop c as least significant term
mymod <- lm(my$mmaxrate ~ my$treatment, weights = my$rmaxrate)
summary(mymod) # FINAL MODEL!
##
## Call:
## lm(formula = my$mmaxrate ~ my$treatment, weights = my$rmaxrate)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -4.0806 -0.8103 -0.1020 0.8827 4.2056
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.009582 0.001258 7.620
## my$treatmentCM+Ethanoldiploid0.001 -0.002035 0.001698 -1.199
## my$treatmentCM+Saltdiploid0.001 0.003154 0.001986 1.588
## my$treatmentCMdiploid0.00025 0.006305 0.002894 2.178
## my$treatmentCMdiploid0.004 -0.002101 0.001904 -1.104
## my$treatmentCMhaploid0.001 -0.002801 0.001837 -1.525
## Pr(>|t|)
## (Intercept) 0.0000000000916 ***
## my$treatmentCM+Ethanoldiploid0.001 0.2347
## my$treatmentCM+Saltdiploid0.001 0.1168
## my$treatmentCMdiploid0.00025 0.0328 *
## my$treatmentCMdiploid0.004 0.2736
## my$treatmentCMhaploid0.001 0.1319
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.479 on 70 degrees of freedom
## Multiple R-squared: 0.2098, Adjusted R-squared: 0.1534
## F-statistic: 3.718 on 5 and 70 DF, p-value: 0.004837
anova(mymod) # test term significance with an lm hist(residuals(mymod), breaks = 25)
hist(expand_residuals(mymod, my$rmaxrate), breaks = 25) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here. 
plot(mymod) # check model fit 
summary(mymod)$r.squared # for R2 value
## [1] 0.2098238
summary(mymod)$adj.r.squared # for adjusted R2 value
## [1] 0.1533827
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_20.html")| Â | my$mmaxrate | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| (Intercept) | 0.00958 | 0.00712 – 0.01205 | 7.61961 | <0.001 |
| CM+Ethanoldiploid 0.001 | -0.00204 | -0.00536 – 0.00129 | -1.19871 | 0.23468475 |
| CM+Saltdiploid 0.001 | 0.00315 | -0.00074 – 0.00705 | 1.58776 | 0.11684830 |
| C Mdiploid 0.00025 | 0.00630 | 0.00063 – 0.01198 | 2.17830 | 0.03275392 |
| C Mdiploid 0.004 | -0.00210 | -0.00583 – 0.00163 | -1.10353 | 0.27357441 |
| C Mhaploid 0.001 | -0.00280 | -0.00640 – 0.00080 | -1.52455 | 0.13187664 |
| Observations | 76 | |||
| R2 / R2 adjusted | 0.210 / 0.153 | |||
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- my$treatment
myy <- my$mmaxrate
myr <- my$rmaxrate
myc <- my$treatment
mytitle <- NULL
myylab <- "M-MAX-RATE"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myptalpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
geom_hline(yintercept = 0, lty = "dotted") + coord_flip() + theme_classic() +
theme(legend.position = "none") + ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) +
scale_x_discrete(labels = c("CM \n Diploid \n 0.001", "CM+EtOH \n Diploid \n 0.001",
"CM+NaCl \n Diploid \n 0.001", "CM \n Diploid \n 0.00025", "CM \n Diploid \n 0.004",
"CM \n Haploid \n 0.001")) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0)) + annotate("text",
x = c(4), y = c(0.0325), label = "*", size = 8, fontface = "bold")
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_21.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, p, myc, myfill, myheight, myoutline, mypalette, myptalpha, myptsize,
myr, mytextsize, mytitle, mywidth, myx, myxlab, myy, myylab)Analysis: Assess effects of evolutionary treatment on T-MAX-DIFF (generation of maximum difference in sympatric barcoded strain abundance)
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ---------------------------------------------------- with initial
# proportions included in the model: mymod <- glm(my$tmaxdiff ~ my$treatment
# + my$ccmaxdiff_t0 + my$diff0, weights = my$rmaxdiff_rt0); summary(mymod) #
# drop c as least significant term
mymod <- lm(my$tmaxdiff ~ my$treatment + my$diff0, weights = my$rmaxdiff_rt0)
summary(mymod) # # FINAL MODEL!
##
## Call:
## lm(formula = my$tmaxdiff ~ my$treatment + my$diff0, weights = my$rmaxdiff_rt0)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -58426 -5302 574 8332 59655
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 241.970 34.101 7.096
## my$treatmentCM+Ethanoldiploid0.001 -22.732 35.785 -0.635
## my$treatmentCM+Saltdiploid0.001 -5.533 38.822 -0.143
## my$treatmentCMdiploid0.00025 11.312 77.413 0.146
## my$treatmentCMdiploid0.004 -23.716 63.676 -0.372
## my$treatmentCMhaploid0.001 1.612 35.479 0.045
## my$diff0 -328.617 81.224 -4.046
## Pr(>|t|)
## (Intercept) 0.000000000892 ***
## my$treatmentCM+Ethanoldiploid0.001 0.527381
## my$treatmentCM+Saltdiploid0.001 0.887074
## my$treatmentCMdiploid0.00025 0.884245
## my$treatmentCMdiploid0.004 0.710705
## my$treatmentCMhaploid0.001 0.963902
## my$diff0 0.000134 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19160 on 69 degrees of freedom
## Multiple R-squared: 0.2342, Adjusted R-squared: 0.1676
## F-statistic: 3.517 on 6 and 69 DF, p-value: 0.004283
anova(mymod) # test term significance with an lm hist(residuals(mymod), breaks = 25)
hist(expand_residuals(mymod, my$rtcc), breaks = 25) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here. 
plot(mymod) # check model fit 
summary(mymod)$r.squared # for R2 value
## [1] 0.2342223
summary(mymod)$adj.r.squared # for adjusted R2 value
## [1] 0.1676329
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_22.html")|  | my\(tmaxdiff</th> </tr> <tr> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; text-align:left; ">Predictors</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">Estimates</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">CI</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">Statistic</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">p</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">(Intercept)</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">241.97046</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">175.13346 – 308.80745</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">7.09567</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; "><strong><0.001</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">CM+Ethanoldiploid 0.001</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-22.73154</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-92.86866 – 47.40558</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.63523</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.52738134</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">CM+Saltdiploid 0.001</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-5.53349</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-81.62393 – 70.55695</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.14253</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.88707398</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">C Mdiploid 0.00025</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">11.31234</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-140.41438 – 163.03905</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.14613</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.88424511</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">C Mdiploid 0.004</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-23.71583</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-148.51929 – 101.08763</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.37244</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.71070458</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">C Mhaploid 0.001</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">1.61153</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-67.92574 – 71.14880</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.04542</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.96390191</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">my\)diff 0 | -328.61667 | -487.81355 – -169.41979 | -4.04579 | 0.00013406 | |||
|---|---|---|---|---|---|---|---|---|
| Observations | 76 | |||||||
| R2 / R2 adjusted | 0.234 / 0.168 | |||||||
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- my$treatment
myy <- my$tmaxdiff
myr <- my$rmaxdiff_rt0
myc <- my$treatment
mytitle <- NULL
myylab <- "T-MAX-DIFF"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myptalpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
geom_hline(yintercept = 0, lty = "dotted") + coord_flip() + theme_classic() +
theme(legend.position = "none") + ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) +
scale_x_discrete(labels = c("CM \n Diploid \n 0.001", "CM+EtOH \n Diploid \n 0.001",
"CM+NaCl \n Diploid \n 0.001", "CM \n Diploid \n 0.00025", "CM \n Diploid \n 0.004",
"CM \n Haploid \n 0.001")) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0))
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_23.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, p, myc, myfill, myheight, myoutline, mypalette, myptalpha, myptsize,
myr, mytextsize, mytitle, mywidth, myx, myxlab, myy, myylab)Analysis: Assess effects of evolutionary treatment on M-MAX-DIFF (magnitude of maximum difference in sympatric barcoded strain abundance)
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ---------------------------------------------------- with initial
# proportions included in the model: mymod <- glm(my$mmaxdiff ~ my$treatment
# + my$ccmaxdiff_t0 + my$diff0, weights = my$rmaxdiff_rt0); summary(mymod) #
# drop c as least significant term
mymod <- lm(my$mmaxdiff ~ my$treatment + my$diff0, weights = my$rmaxdiff_rt0)
summary(mymod) # # FINAL MODEL!
##
## Call:
## lm(formula = my$mmaxdiff ~ my$treatment + my$diff0, weights = my$rmaxdiff_rt0)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -90.008 -8.179 2.215 10.833 47.792
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.18985 0.04004 4.741 0.00001105
## my$treatmentCM+Ethanoldiploid0.001 0.00454 0.04202 0.108 0.914271
## my$treatmentCM+Saltdiploid0.001 0.17472 0.04559 3.833 0.000277
## my$treatmentCMdiploid0.00025 0.15524 0.09090 1.708 0.092165
## my$treatmentCMdiploid0.004 0.03311 0.07477 0.443 0.659311
## my$treatmentCMhaploid0.001 0.20429 0.04166 4.904 0.00000601
## my$diff0 0.29644 0.09538 3.108 0.002734
##
## (Intercept) ***
## my$treatmentCM+Ethanoldiploid0.001
## my$treatmentCM+Saltdiploid0.001 ***
## my$treatmentCMdiploid0.00025 .
## my$treatmentCMdiploid0.004
## my$treatmentCMhaploid0.001 ***
## my$diff0 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.49 on 69 degrees of freedom
## Multiple R-squared: 0.4789, Adjusted R-squared: 0.4336
## F-statistic: 10.57 on 6 and 69 DF, p-value: 0.00000002701
anova(mymod) # test term significance with an lm hist(residuals(mymod), breaks = 25)
hist(expand_residuals(mymod, my$rtcc), breaks = 25) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here. 
plot(mymod) # check model fit 
summary(mymod)$r.squared # for R2 value
## [1] 0.4789295
summary(mymod)$adj.r.squared # for adjusted R2 value
## [1] 0.433619
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_24.html")|  | my\(mmaxdiff</th> </tr> <tr> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; text-align:left; ">Predictors</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">Estimates</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">CI</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">Statistic</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">p</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">(Intercept)</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.18985</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.11137 – 0.26833</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">4.74128</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; "><strong>0.00001105</strong></td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">CM+Ethanoldiploid 0.001</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.00454</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.07782 – 0.08690</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.10805</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.91427138</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">CM+Saltdiploid 0.001</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.17472</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.08537 – 0.26406</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">3.83269</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; "><strong>0.00027655</strong></td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">C Mdiploid 0.00025</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.15524</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.02292 – 0.33340</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">1.70782</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.09216521</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">C Mdiploid 0.004</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.03311</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.11344 – 0.17965</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.44278</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.65931134</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">C Mhaploid 0.001</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.20429</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.12264 – 0.28594</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">4.90375</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; "><strong>0.00000601</strong></td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">my\)diff 0 | 0.29644 | 0.10951 – 0.48337 | 3.10815 | 0.00273414 | |||
|---|---|---|---|---|---|---|---|---|
| Observations | 76 | |||||||
| R2 / R2 adjusted | 0.479 / 0.434 | |||||||
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- my$treatment
myy <- my$mmaxdiff
myr <- my$rmaxdiff_rt0
myc <- my$treatment
mytitle <- NULL
myylab <- "M-MAX-DIFF"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myptalpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
geom_hline(yintercept = 0, lty = "dotted") + coord_flip() + theme_classic() +
theme(legend.position = "none") + ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) +
scale_x_discrete(labels = c("CM \n Diploid \n 0.001", "CM+EtOH \n Diploid \n 0.001",
"CM+NaCl \n Diploid \n 0.001", "CM \n Diploid \n 0.00025", "CM \n Diploid \n 0.004",
"CM \n Haploid \n 0.001")) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0)) + annotate("text",
x = c(3, 6), y = c(0.525), label = "***", size = 8, fontface = "bold")
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_25.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, p, myc, myfill, myheight, myoutline, mypalette, myptalpha, myptsize,
myr, mytextsize, mytitle, mywidth, myx, myxlab, myy, myylab)Analysis: Assess effects of evolutionary treatment on total change in barcode abundance (summed across six intervals)
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ---------------------------------------------------- with initial
# proportions included in the model: mymod <- glm(my$tcc ~ my$treatment +
# my$cctcc_t0 + my$diff0, weights = my$rtcc_rt0); summary(mymod) # drop c as
# least significant term mymod <- glm(my$tcc ~ my$treatment + my$diff0,
# weights = my$rtcc_rt0); summary(mymod) # diff 0 still not
# significant...run the model set without it.
# without: mymod <- glm(my$tcc ~ my$treatment + my$cctcc, weights =
# my$rtcc); summary(mymod) # drop c as least significant term
mymod <- lm(my$tcc ~ my$treatment, weights = my$rtcc)
summary(mymod) # FINAL MODEL!
##
## Call:
## lm(formula = my$tcc ~ my$treatment, weights = my$rtcc)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -5.7198 -1.3007 -0.0058 1.0657 5.4905
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.0219194 0.0027409 7.997
## my$treatmentCM+Ethanoldiploid0.001 -0.0072401 0.0032633 -2.219
## my$treatmentCM+Saltdiploid0.001 0.0046752 0.0035042 1.334
## my$treatmentCMdiploid0.00025 0.0182730 0.0056216 3.251
## my$treatmentCMdiploid0.004 -0.0007325 0.0043115 -0.170
## my$treatmentCMhaploid0.001 -0.0066256 0.0033076 -2.003
## Pr(>|t|)
## (Intercept) 0.0000000000185 ***
## my$treatmentCM+Ethanoldiploid0.001 0.02976 *
## my$treatmentCM+Saltdiploid0.001 0.18647
## my$treatmentCMdiploid0.00025 0.00177 **
## my$treatmentCMdiploid0.004 0.86559
## my$treatmentCMhaploid0.001 0.04904 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.15 on 70 degrees of freedom
## Multiple R-squared: 0.3746, Adjusted R-squared: 0.33
## F-statistic: 8.387 on 5 and 70 DF, p-value: 0.000002951
anova(mymod) # test term significance with an lm hist(residuals(mymod), breaks = 25)
hist(expand_residuals(mymod, my$rtcc), breaks = 25) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here. 
plot(mymod) # check model fit 
summary(mymod)$r.squared # for R2 value
## [1] 0.3746437
summary(mymod)$adj.r.squared # for adjusted R2 value
## [1] 0.3299754
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_26.html")| Â | my$tcc | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| (Intercept) | 0.02192 | 0.01655 – 0.02729 | 7.99719 | <0.001 |
| CM+Ethanoldiploid 0.001 | -0.00724 | -0.01364 – -0.00084 | -2.21861 | 0.02975813 |
| CM+Saltdiploid 0.001 | 0.00468 | -0.00219 – 0.01154 | 1.33418 | 0.18646947 |
| C Mdiploid 0.00025 | 0.01827 | 0.00725 – 0.02929 | 3.25053 | 0.00177244 |
| C Mdiploid 0.004 | -0.00073 | -0.00918 – 0.00772 | -0.16989 | 0.86559011 |
| C Mhaploid 0.001 | -0.00663 | -0.01311 – -0.00014 | -2.00311 | 0.04903884 |
| Observations | 76 | |||
| R2 / R2 adjusted | 0.375 / 0.330 | |||
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- my$treatment
myy <- my$tcc
myr <- my$rtcc
myc <- my$treatment
mytitle <- NULL
myylab <- "Total Change in Abundance"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myptalpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
geom_hline(yintercept = 0, lty = "dotted") + coord_flip() + theme_classic() +
theme(legend.position = "none") + ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) +
scale_x_discrete(labels = c("CM \n Diploid \n 0.001", "CM+EtOH \n Diploid \n 0.001",
"CM+NaCl \n Diploid \n 0.001", "CM \n Diploid \n 0.00025", "CM \n Diploid \n 0.004",
"CM \n Haploid \n 0.001")) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0)) + annotate("text",
x = c(2, 4, 6), y = c(0.0275, 0.06825, 0.026), label = c("*", "**", "*"),
size = 8, fontface = "bold")
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_27.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, p, myc, myfill, myheight, myoutline, mypalette, myptalpha, myptsize,
myr, mytextsize, mytitle, mywidth, myx, myxlab, myy, myylab)